1 General introduction
1.1 Introduction
Linear Mixed Model were used.
1.2 Experiment design
- Independent variables:
- Stimulus Type (
Type: intact vs. scrambled) - Stimulus Category (
Category: faces vs. houses) - Hemisphere (
Hemisphere: left vs. right) - Duration (
Duration: 17 vs. 200)
- Stimulus Type (
1.3 Preparations
## load libraries
library(tidyverse)
library(magrittr)
library(lme4)
library(lmerTest)
library(emmeans)
# options for calculating estimated marginal means
# lmer.df = c("kenward-roger", "satterthwaite", "asymptotic")
emm_options(lmer.df = "satterthwaite",
lmerTest.limit = 1e6,
opt.digits = FALSE) # , pbkrtest.limit =, disable.pbkrtest = FALSE# general setting
saveCSV <- FALSE
ratio.count <- 0.8
RT.min <- 200
# set up the theme for plot and rainclound plot
plot_theme <- {
general_theme +
theme(legend.position = "right")
}
erp_theme <- {
general_theme +
theme(legend.position = "bottom")
}
ylimit.p1 <- c(2.5, -0.5)
ylimit.n170 <- c(1.5, -5.5) # y axis for plotting N170 amplitude
resp.labels = c("sure faces", "not sure faces", "guessing", "not sure houses", "sure houses")2 Hypotheses
Previous studies showed that the N170 amplitudes evoked by faces preseneted for longer durations were larger:
Duration <- c("17", "200")
Amplitudes <- c(-2.5, -5)
Hypotheses <- rep("means", 2)
df.lit <- data_frame(Duration, Amplitudes, Hypotheses)## Warning: `data_frame()` is deprecated, use `tibble()`.
## This warning is displayed once per session.plot.lit <- {
ggplot(data = df.lit, aes(y = Amplitudes, x = Duration, fill = Duration)) + # set the data, varialbes for x and y axises, and the variable for dividing data
geom_col(color = "black") +
facet_grid(. ~ Hypotheses) +
scale_fill_manual(values = c(gray(.7), gray(.3))) +
scale_y_reverse(limits =c(0, -5.5), breaks = seq(0, -5, -1), expand= c(0, 0)) + # set the limit for y axis
geom_hline(yintercept = -2.5, linetype = "dashed", color = "red", size = 1) +
labs(x = "", y = expression(paste("Amplitudes (", mu, "V)"))) + # set the names for main, x and y axises , title = "Means",
# theme_bw() +
general_theme +
theme( # strip.text = element_text(color = "white"),
legend.position = "none",
axis.line = element_line(size = 3, colour = "black"))
}
plot.lit
There are two possible explainations for the aggregated mean amplitudes:
Duration <- rep(c(rep("17", 5), rep("200", 5) ), 2)
Amplitudes <- c(c(rep(-2.5, 5), rep(-5, 5)), c(rep(-.83, 3), rep(-5, 7)))
TrialNum <- rep(as.character(rep(c(1:5), 2)), 2)
Hypotheses <- c(rep("Possibility 1", 10), rep("Possibility 2", 10))
df.st <- data_frame(Duration, Amplitudes, TrialNum, Hypotheses)
plot.st <- {
ggplot(data = df.st, aes(y = Amplitudes, x = Duration, fill = Duration, color = TrialNum)) + # set the data, varialbes for x and y axises, and the variable for dividing data
geom_col(position = "dodge") +
scale_fill_manual(values = c(gray(.7), gray(.3))) +
scale_color_manual(values = rep("black", 5)) +
facet_grid(. ~ Hypotheses) +
geom_hline(yintercept = -2.5, linetype = "dashed", color = "red", size = 1) +
scale_y_reverse(limits =c(0, -5.5), breaks = seq(0, -5, -1), expand= c(0, 0)) + # set the limit for y axis
labs(x = "Duration (ms)") + # set the names for main, x and y axises , title = "At the single-trial level:", , y = expression(paste("Amplitudes (", mu, "V)"))
theme_bw() +
general_theme +
theme(legend.position = "none",
axis.title.y = element_blank(),
axis.line = element_line(size = 3, colour = "black"))
}
plot.st
3 Behavior results
raw.beha.205 <- {
"E205_35_Behavior.csv" %>%
file.path(folder.data, .) %>%
read_csv()
}## Parsed with column specification:
## cols(
## .default = col_double(),
## ExperimentName = col_character(),
## Clock.Information = col_character(),
## Handedness = col_character(),
## ip_Address = col_character(),
## ResearcherID = col_character(),
## SessionDate = col_character(),
## SessionTime = col_time(format = ""),
## SessionTimeUtc = col_character(),
## Sex = col_character(),
## `FH[Block]` = col_character(),
## `Procedure[Block]` = col_character(),
## `Running[Block]` = col_character(),
## scrambleVar = col_character(),
## `stimCategory[Block]` = col_character(),
## `stimName[Block]` = col_character(),
## CellLabel = col_character(),
## `FH[Trial]` = col_character(),
## `Procedure[Trial]` = col_character(),
## `Running[Trial]` = col_character(),
## `stimCategory[Trial]` = col_character()
## # ... with 1 more columns
## )## See spec(...) for full column specifications.clean.beha.205 <- {
raw.beha.205 %>%
filter(`Procedure[Trial]` == "expProc") %>% # remove rows for practice and countdown
select(ExperimentName, SubjCode = Subject, blockID, Block, Trial, Age, Sex, Handedness, Type = `stimCategory[Trial]`, Category = `FH[Trial]`, Duration = `stimDuration[Trial]`, ACC = `Resp.ACC[Trial]`, Resp.RT = `Resp.RT[Trial]`, Resp = `Resp.RESP[Trial]`, Stimuli = `stimName[Trial]`) %>% # select and rename the columns (remove unuseful columns)
mutate(
SubjCode = factor(paste("P", SubjCode, sep = "")), # save SubjCode as factor
Type = substr(Type, 1, 1),
Type = recode_factor(Type, "N" = "intact", "S" = "scrambled"), # rename the levels of Type
Category = recode(Category, "hosue" = "house", "house" = "house", "face" = "face", .default = "face"), # rename "hosue" as "house"
Duration = factor(Duration), # save Duration as factor
Category = as.factor(Category),
Resp = as.factor(Resp),
blockID = as.factor(blockID)
) %>%
group_by(SubjCode, Type, Category, Duration, ACC) %>% # divide the data based on these conditions
mutate(
RT = Resp.RT + as.numeric(levels(Duration))[Duration],
RT.Z = scale(RT), # calculate the Z value for reaction times within each group
RT.Within3Z = ifelse(RT.Z <= 3 & RT.Z >= -3, 1, ifelse(RT.Z < -3 | RT.Z > 3, 0, NaN)) # if the Z values are between -3 and 3, will be marked as 1
) %>%
ungroup() # ungroup the data
}
# behavior.tidyup
head(clean.beha.205, 10)# check the number of trials for 205
sum.count.205 <- {
clean.beha.205 %>%
group_by(SubjCode, Type, Category, Duration) %>%
summarize(Count_sum = n())
}
valid.count.205 <- {
clean.beha.205 %>%
filter(RT > RT.min) %>%
group_by(SubjCode, Type, Category, Duration) %>%
summarize(Count = n()) %>%
right_join(sum.count.205, by = c("SubjCode", "Type", "Category", "Duration")) %>%
mutate(ratio = Count / Count_sum) %>%
filter(ratio > ratio.count) %$%
SubjCode %>%
unique()
}
# remove the participants
clean.beha.205 %<>% filter(SubjCode %in% valid.count.205)# remove participants whose performance in Key 1 was lower than 95%
valid.17 <- {
clean.beha.205 %>%
filter(Duration == 17, Resp == 1) %>% # Type == "intact",
group_by(SubjCode, Duration, Resp) %>% # Type,
summarize(Accuracy = mean(ACC), Count = n()) %>%
filter(Accuracy > 0.95) %$%
SubjCode
}
# clean.beha.205 %>%
# filter(Duration == 17, Resp == 1) %>% # Type == "intact",
# group_by(SubjCode, Duration, Resp) %>% # Type,
# summarize(Accuracy = mean(ACC), Count = n()) %>%
# filter(Accuracy < 0.95) %>%
# ungroup() %>%
# summarize(mean(Accuracy), min(Accuracy), max(Accuracy))
clean.beha.205 %<>% filter(SubjCode %in% valid.17)3.1 Participant demographic information
subj.info.205 <- {
clean.beha.205 %>%
select(SubjCode, Age, Sex, Handedness) %>% # select the columns of participant inforamtion
distinct() # only keep the unique rows
}
subj.info.205mean(subj.info.205$Age)## [1] 22.32143range(subj.info.205$Age)## [1] 18 32sum(subj.info.205$Sex == "female")## [1] 203.2 Responses
tmp.count <- {
clean.beha.205 %>%
group_by(SubjCode, Type, Category, Duration) %>%
summarize(Count_sum = n())
}
avg.dist.long.205 <- {
clean.beha.205 %>%
# mutate(RespRateFace = if_else(Category == "face", ACC, if_else(Category == "house", as.numeric(!ACC), NaN))) %>%
group_by(SubjCode, Type, Category, Duration, Resp) %>%
summarize(Count = n()) %>%
right_join(tmp.count, by = c("SubjCode", "Type", "Category", "Duration")) %>%
mutate(RespRate = Count / Count_sum) %>%
ungroup()
}
# descriptive statistics of accuracy for plotting
desc.dist.205 <- {
avg.dist.long.205 %>%
group_by(Type, Category, Duration, Resp) %>%
summarize(Mean = mean(RespRate)
, N = n()
, SD = if_else(N > 1, sd(RespRate), 0)
, SE = SD/sqrt(N)
, SE.lo = Mean - SE, SE.hi = Mean + SE
, CI_lo = Mean - SE * 1.96
, CI_hi = Mean + SE * 1.96
, Median = median(RespRate)
, Lower = Mean - SD, Upper = Mean + SD # for rainclound plot
) %>%
ungroup() %>%
add_row(Type = "intact", Category = "house", Duration = 200, Resp = 2, Mean = 0, N = 0, SD = 0, SE = 0, SE.lo = 0,
SE.hi = 0, CI_lo = 0, CI_hi = 0, Median = 0, Lower = 0, Upper = 0)
}
avg.dist.wide.205 <- {
avg.dist.long.205 %>%
mutate(Conditions = paste(Type, Category, Duration, Resp, sep = "_")) %>%
select(SubjCode, Conditions, RespRate) %>%
spread(Conditions, RespRate)
}knitr::kable(desc.dist.205, digits = 4)| Type | Category | Duration | Resp | Mean | N | SD | SE | SE.lo | SE.hi | CI_lo | CI_hi | Median | Lower | Upper |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| intact | face | 17 | 1 | 0.4688 | 28 | 0.3306 | 0.0625 | 0.4063 | 0.5312 | 0.3463 | 0.5912 | 0.4469 | 0.1381 | 0.7994 |
| intact | face | 17 | 2 | 0.3205 | 28 | 0.2276 | 0.0430 | 0.2775 | 0.3636 | 0.2362 | 0.4049 | 0.3469 | 0.0929 | 0.5482 |
| intact | face | 17 | 3 | 0.1593 | 28 | 0.1631 | 0.0308 | 0.1284 | 0.1901 | 0.0988 | 0.2197 | 0.0969 | -0.0039 | 0.3224 |
| intact | face | 17 | 4 | 0.0478 | 28 | 0.0565 | 0.0107 | 0.0371 | 0.0584 | 0.0268 | 0.0687 | 0.0219 | -0.0087 | 0.1043 |
| intact | face | 17 | 5 | 0.0086 | 12 | 0.0129 | 0.0037 | 0.0049 | 0.0123 | 0.0013 | 0.0159 | 0.0031 | -0.0043 | 0.0215 |
| intact | face | 200 | 1 | 0.9768 | 28 | 0.0320 | 0.0061 | 0.9707 | 0.9828 | 0.9649 | 0.9887 | 0.9875 | 0.9447 | 1.0088 |
| intact | face | 200 | 2 | 0.0400 | 10 | 0.0403 | 0.0127 | 0.0273 | 0.0527 | 0.0150 | 0.0650 | 0.0188 | -0.0003 | 0.0803 |
| intact | face | 200 | 3 | 0.0208 | 9 | 0.0125 | 0.0042 | 0.0167 | 0.0250 | 0.0127 | 0.0290 | 0.0125 | 0.0083 | 0.0333 |
| intact | face | 200 | 4 | 0.0125 | 1 | 0.0000 | 0.0000 | 0.0125 | 0.0125 | 0.0125 | 0.0125 | 0.0125 | 0.0125 | 0.0125 |
| intact | face | 200 | 5 | 0.0125 | 4 | 0.0000 | 0.0000 | 0.0125 | 0.0125 | 0.0125 | 0.0125 | 0.0125 | 0.0125 | 0.0125 |
| intact | house | 17 | 1 | 0.0094 | 18 | 0.0086 | 0.0020 | 0.0073 | 0.0114 | 0.0054 | 0.0134 | 0.0062 | 0.0007 | 0.0180 |
| intact | house | 17 | 2 | 0.0550 | 28 | 0.0677 | 0.0128 | 0.0422 | 0.0678 | 0.0300 | 0.0801 | 0.0328 | -0.0126 | 0.1227 |
| intact | house | 17 | 3 | 0.3872 | 28 | 0.2582 | 0.0488 | 0.3384 | 0.4360 | 0.2915 | 0.4828 | 0.3234 | 0.1289 | 0.6454 |
| intact | house | 17 | 4 | 0.3988 | 28 | 0.2211 | 0.0418 | 0.3570 | 0.4406 | 0.3169 | 0.4807 | 0.4047 | 0.1776 | 0.6199 |
| intact | house | 17 | 5 | 0.1587 | 27 | 0.2304 | 0.0443 | 0.1143 | 0.2030 | 0.0718 | 0.2456 | 0.0438 | -0.0718 | 0.3891 |
| intact | house | 200 | 1 | 0.0188 | 6 | 0.0105 | 0.0043 | 0.0145 | 0.0230 | 0.0104 | 0.0271 | 0.0125 | 0.0083 | 0.0292 |
| intact | house | 200 | 3 | 0.0150 | 10 | 0.0079 | 0.0025 | 0.0125 | 0.0175 | 0.0101 | 0.0199 | 0.0125 | 0.0071 | 0.0229 |
| intact | house | 200 | 4 | 0.0331 | 20 | 0.0208 | 0.0046 | 0.0285 | 0.0378 | 0.0240 | 0.0422 | 0.0250 | 0.0123 | 0.0539 |
| intact | house | 200 | 5 | 0.9670 | 28 | 0.0262 | 0.0049 | 0.9620 | 0.9719 | 0.9573 | 0.9767 | 0.9750 | 0.9408 | 0.9931 |
| scrambled | face | 17 | 1 | 0.0156 | 12 | 0.0126 | 0.0036 | 0.0120 | 0.0193 | 0.0085 | 0.0228 | 0.0125 | 0.0030 | 0.0283 |
| scrambled | face | 17 | 2 | 0.0467 | 28 | 0.0538 | 0.0102 | 0.0365 | 0.0568 | 0.0267 | 0.0666 | 0.0375 | -0.0072 | 0.1005 |
| scrambled | face | 17 | 3 | 0.7143 | 28 | 0.2133 | 0.0403 | 0.6740 | 0.7546 | 0.6353 | 0.7933 | 0.7781 | 0.5010 | 0.9276 |
| scrambled | face | 17 | 4 | 0.2016 | 28 | 0.1637 | 0.0309 | 0.1706 | 0.2325 | 0.1409 | 0.2622 | 0.1656 | 0.0378 | 0.3653 |
| scrambled | face | 17 | 5 | 0.0616 | 14 | 0.1038 | 0.0277 | 0.0339 | 0.0893 | 0.0072 | 0.1160 | 0.0156 | -0.0422 | 0.1654 |
| scrambled | face | 200 | 1 | 0.0188 | 2 | 0.0088 | 0.0062 | 0.0125 | 0.0250 | 0.0065 | 0.0310 | 0.0188 | 0.0099 | 0.0276 |
| scrambled | face | 200 | 2 | 0.0292 | 12 | 0.0268 | 0.0077 | 0.0214 | 0.0369 | 0.0140 | 0.0443 | 0.0125 | 0.0023 | 0.0560 |
| scrambled | face | 200 | 3 | 0.9754 | 28 | 0.0332 | 0.0063 | 0.9692 | 0.9817 | 0.9631 | 0.9878 | 0.9875 | 0.9422 | 1.0087 |
| scrambled | face | 200 | 4 | 0.0216 | 11 | 0.0159 | 0.0048 | 0.0168 | 0.0264 | 0.0122 | 0.0310 | 0.0125 | 0.0057 | 0.0375 |
| scrambled | face | 200 | 5 | 0.0312 | 2 | 0.0265 | 0.0188 | 0.0125 | 0.0500 | -0.0055 | 0.0680 | 0.0312 | 0.0047 | 0.0578 |
| scrambled | house | 17 | 1 | 0.0144 | 10 | 0.0084 | 0.0026 | 0.0117 | 0.0170 | 0.0092 | 0.0196 | 0.0125 | 0.0060 | 0.0227 |
| scrambled | house | 17 | 2 | 0.0486 | 27 | 0.0520 | 0.0100 | 0.0386 | 0.0586 | 0.0290 | 0.0682 | 0.0375 | -0.0034 | 0.1006 |
| scrambled | house | 17 | 3 | 0.7002 | 28 | 0.2218 | 0.0419 | 0.6583 | 0.7421 | 0.6181 | 0.7824 | 0.7375 | 0.4785 | 0.9220 |
| scrambled | house | 17 | 4 | 0.2080 | 28 | 0.1729 | 0.0327 | 0.1754 | 0.2407 | 0.1440 | 0.2721 | 0.1531 | 0.0351 | 0.3809 |
| scrambled | house | 17 | 5 | 0.0695 | 16 | 0.1204 | 0.0301 | 0.0394 | 0.0996 | 0.0105 | 0.1285 | 0.0219 | -0.0509 | 0.1899 |
| scrambled | house | 200 | 1 | 0.0125 | 4 | 0.0000 | 0.0000 | 0.0125 | 0.0125 | 0.0125 | 0.0125 | 0.0125 | 0.0125 | 0.0125 |
| scrambled | house | 200 | 2 | 0.0250 | 11 | 0.0256 | 0.0077 | 0.0173 | 0.0327 | 0.0099 | 0.0401 | 0.0125 | -0.0006 | 0.0506 |
| scrambled | house | 200 | 3 | 0.9732 | 28 | 0.0345 | 0.0065 | 0.9667 | 0.9797 | 0.9604 | 0.9860 | 0.9750 | 0.9387 | 1.0077 |
| scrambled | house | 200 | 4 | 0.0240 | 12 | 0.0135 | 0.0039 | 0.0200 | 0.0279 | 0.0163 | 0.0316 | 0.0188 | 0.0104 | 0.0375 |
| scrambled | house | 200 | 5 | 0.0458 | 3 | 0.0473 | 0.0273 | 0.0185 | 0.0732 | -0.0077 | 0.0994 | 0.0250 | -0.0015 | 0.0932 |
| intact | house | 200 | 2 | 0.0000 | 0 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
dist.RainPlot.205 <- {
ggplot(data = avg.dist.long.205, aes(y = RespRate, x = as.factor(Resp), fill = Category)) +
geom_flat_violin(position = position_nudge(x = .2, y = 0), alpha = .7) +
geom_point(aes(y = RespRate, color = Category), position = position_jitter(width = .15), size = .5, alpha = .8) +
geom_boxplot(aes(y = RespRate), width = .2, outlier.shape = NA, alpha = .7) +
geom_point(data = desc.dist.205, aes(y = Mean, x = Resp, color = Category), position = position_nudge(x = 0.3), size = 2.5) +
geom_errorbar(data = desc.dist.205, aes(y = Mean, ymin = Lower, ymax = Upper), position = position_nudge(x = 0.3), width = 0) +
facet_grid(Type ~ Duration) +
# facet_grid(. ~ Type, switch = "x") + # create two panels to show data
# scale_colour_grey() + # start = .1, end = .6, color for the contour
# scale_fill_grey() + # start = .3, end = .6, color for the fill
# scale_color_brewer(palette = "Set1") + # palette = "RdBu"
# scale_fill_brewer(palette = "Set1") + # palette = "RdBu"
labs(title = "Reaction times for 205", x = "Stimulus Type", y = "Reaction times (ms)", fill = "Duration(ms)", color = "Duration(ms)") + # set the names for main, x and y axises and the legend
# scale_x_discrete(labels=resp.labels) +
# coord_cartesian(ylim = c(0, 1.05)) + # set the limit for y axis
# scale_y_continuous(expand= c(0, 0)) + # remove the space between columns and x axis
theme_bw() + # the backgroud color
plot_theme
}
dist.RainPlot.205
dura.labels <- c("17ms", "200ms")
names(dura.labels) <- c(17, 200)
dist.ColuPlot.205 <- {
ggplot(data = desc.dist.205, aes(y = Mean, x = Resp, fill = Category)) + # set the data, varialbes for x and y axises, and the variable for dividing data
geom_col(position = "dodge", color = "black", alpha = .7) + # position of columns and countour of columns
facet_grid(Type ~ Duration,
labeller = labeller(Duration = dura.labels)) +
geom_errorbar(mapping = aes(ymin = CI_lo, ymax = CI_hi), linetype = 1, # set the error bar
show.legend = FALSE, width = 0.25, alpha = .5,
position = position_dodge(width=0.9)) +
geom_hline(yintercept = c(1), linetype = 5, alpha = 0.5) + # add the line for 0.5 and 1 (y)
coord_cartesian(ylim = c(0,1.1)) + # set the limit for y axis
scale_y_continuous(expand= c(0, 0)) + # remove the space between columns and x axis
labs(title = "", x = "Responses", y = "Percentage", fill = "Category") + # set the names for main, x and y axises Subjective Responses for E205
scale_fill_grey() + # set the color for columns
# geom_text(label = c("***", "***", "", ""), color = c("red", "red", "red", "red"), size = 6, nudge_y = 0.05, nudge_x = rep(c(-0.225, 0.225), 2)) + # add starts to the significant columns
erp_theme
}
dist.ColuPlot.205
df.ratio.205 <- {
clean.beha.205 %>%
mutate(isFace = if_else(Category == "face", 1, 0)) %>%
group_by(SubjCode, Type, Duration, Resp) %>%
summarize(ratio = mean(isFace), Count = n())
}
desc.ratio.205 <- {
df.ratio.205 %>%
group_by(Type, Duration, Resp) %>%
summarize(Mean = mean(ratio)
, N = n()
, SD = if_else(N > 1, sd(ratio), 0)
, SE = SD/sqrt(N)
, SE.lo = Mean - SE, SE.hi = Mean + SE
, CI_lo = Mean - SE * 1.96
, CI_hi = Mean + SE * 1.96
, Median = median(ratio)
, Lower = Mean - SD, Upper = Mean + SD # for rainclound plot
) %>%
ungroup()
}3.2.1 LMM for responses
df.ratio.205 %<>% sdif_coding_P203_resp 3.2.1.1 The maximal model
lmm.max.resp <- lmer(ratio ~ Type * Duration * Resp + (1 + Type_C + Dura_C + Type_Dura | SubjCode),
df.ratio.205)## boundary (singular) fit: see ?isSingular## Warning: Model failed to converge with 1 negative eigenvalue: -1.7e-02print(summary(lmm.max.resp), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: ratio ~ Type * Duration * Resp + (1 + Type_C + Dura_C + Type_Dura | SubjCode)
## Data: df.ratio.205
##
## REML criterion at convergence: -88.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9487 -0.2868 -0.0109 0.1602 3.2327
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjCode (Intercept) 0.0008608 0.02934
## Type_C 0.0009817 0.03133 1.00
## Dura_C 0.0046460 0.06816 0.72 0.72
## Type_Dura 0.0147012 0.12125 0.74 0.74 1.00
## Residual 0.0389778 0.19743
## Number of obs: 428, groups: SubjCode, 28
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.465725 0.012987 48.761546 35.860 < 2e-16 ***
## Type2-1 -0.009004 0.024204 94.303934 -0.372 0.710717
## Duration2-1 -0.020693 0.026829 43.357514 -0.771 0.444706
## Resp2-1 0.001016 0.038252 386.870545 0.027 0.978827
## Resp3-2 -0.281151 0.031701 366.105810 -8.869 < 2e-16 ***
## Resp4-3 -0.175603 0.029923 370.276934 -5.868 9.77e-09 ***
## Resp5-4 -0.062545 0.037613 381.720530 -1.663 0.097157 .
## Type2-1:Duration2-1 -0.154060 0.052316 49.742197 -2.945 0.004903 **
## Type2-1:Resp2-1 0.193941 0.076290 384.365785 2.542 0.011409 *
## Type2-1:Resp3-2 0.493453 0.063544 368.054343 7.765 8.15e-14 ***
## Type2-1:Resp4-3 0.272278 0.059862 370.772265 4.548 7.33e-06 ***
## Type2-1:Resp5-4 -0.087209 0.075176 380.506181 -1.160 0.246751
## Duration2-1:Resp2-1 0.285201 0.076504 386.781947 3.728 0.000222 ***
## Duration2-1:Resp3-2 -0.019693 0.063402 366.105810 -0.311 0.756283
## Duration2-1:Resp4-3 -0.197975 0.059846 370.276934 -3.308 0.001031 **
## Duration2-1:Resp5-4 -0.019521 0.075283 381.730772 -0.259 0.795542
## Type2-1:Duration2-1:Resp2-1 0.165273 0.152574 384.214140 1.083 0.279385
## Type2-1:Duration2-1:Resp3-2 -0.079215 0.127089 368.054342 -0.623 0.533471
## Type2-1:Duration2-1:Resp4-3 0.253131 0.119724 370.772265 2.114 0.035158 *
## Type2-1:Duration2-1:Resp5-4 -0.084537 0.150504 381.224642 -0.562 0.574656
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## convergence code: 0
## boundary (singular) fit: see ?isSingular3.2.1.2 The zero-correlation-parameter model
lmm.zcp.resp <- update(lmm.max.resp,
formula = ratio ~ Type * Duration * Resp +
(1 + Type_C + Dura_C + Type_Dura || SubjCode))## boundary (singular) fit: see ?isSingularprint(summary(lmm.zcp.resp), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: ratio ~ Type + Duration + Resp + (1 + Type_C + Dura_C + Type_Dura ||
## SubjCode) + Type:Duration + Type:Resp + Duration:Resp + Type:Duration:Resp
## Data: df.ratio.205
##
## REML criterion at convergence: -83.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9443 -0.2307 -0.0144 0.1260 3.3470
##
## Random effects:
## Groups Name Variance Std.Dev.
## SubjCode (Intercept) 1.719e-07 0.0004146
## SubjCode.1 Type_C 0.000e+00 0.0000000
## SubjCode.2 Dura_C 1.950e-03 0.0441571
## SubjCode.3 Type_Dura 4.430e-03 0.0665559
## Residual 4.057e-02 0.2014104
## Number of obs: 428, groups: SubjCode, 28
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.469614 0.011565 341.011588 40.607 < 2e-16 ***
## Type2-1 -0.001826 0.023125 394.027144 -0.079 0.937086
## Duration2-1 -0.013445 0.024611 44.049727 -0.546 0.587615
## Resp2-1 -0.004786 0.038271 386.579641 -0.125 0.900541
## Resp3-2 -0.280595 0.032260 374.465657 -8.698 < 2e-16 ***
## Resp4-3 -0.170501 0.030369 370.994699 -5.614 3.87e-08 ***
## Resp5-4 -0.057109 0.037598 380.493348 -1.519 0.129612
## Type2-1:Duration2-1 -0.140084 0.047982 44.817106 -2.919 0.005468 **
## Type2-1:Resp2-1 0.181034 0.076506 386.688023 2.366 0.018460 *
## Type2-1:Resp3-2 0.496577 0.064540 374.371374 7.694 1.28e-13 ***
## Type2-1:Resp4-3 0.280222 0.060739 370.985343 4.614 5.46e-06 ***
## Type2-1:Resp5-4 -0.075634 0.075188 380.497724 -1.006 0.315087
## Duration2-1:Resp2-1 0.276266 0.076546 386.822446 3.609 0.000348 ***
## Duration2-1:Resp3-2 -0.018579 0.064520 374.465655 -0.288 0.773545
## Duration2-1:Resp4-3 -0.187772 0.060738 370.994701 -3.092 0.002142 **
## Duration2-1:Resp5-4 -0.008628 0.075240 384.958509 -0.115 0.908761
## Type2-1:Duration2-1:Resp2-1 0.144795 0.153018 386.881798 0.946 0.344604
## Type2-1:Duration2-1:Resp3-2 -0.072967 0.129080 374.371376 -0.565 0.572219
## Type2-1:Duration2-1:Resp4-3 0.269019 0.121478 370.985343 2.215 0.027398 *
## Type2-1:Duration2-1:Resp5-4 -0.057947 0.150462 384.942732 -0.385 0.700356
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## convergence code: 0
## boundary (singular) fit: see ?isSingular3.2.1.3 The reduced model
summary(rePCA(lmm.zcp.resp))## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4]
## Standard deviation 0.3304 0.2192 0.002058 0
## Proportion of Variance 0.6943 0.3056 0.000030 0
## Cumulative Proportion 0.6943 1.0000 1.000000 1lmm.rdc.resp <- update(lmm.zcp.resp,
formula = ratio ~ Type * Duration * Resp +
(0 + Dura_C + Type_Dura || SubjCode))
anova(lmm.rdc.resp, lmm.zcp.resp, refit = FALSE)3.2.1.4 The extended model
lmm.etd.resp <- update(lmm.zcp.resp,
formula = ratio ~ Type * Duration * Resp +
(0 + Dura_C + Type_Dura | SubjCode))## boundary (singular) fit: see ?isSingularsummary(lmm.etd.resp)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: ratio ~ Type + Duration + Resp + (0 + Dura_C + Type_Dura | SubjCode) +
## Type:Duration + Type:Resp + Duration:Resp + Type:Duration:Resp
## Data: df.ratio.205
##
## REML criterion at convergence: -86
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8966 -0.2787 -0.0083 0.1667 3.3573
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjCode Dura_C 0.003545 0.05954
## Type_Dura 0.011075 0.10524 1.00
## Residual 0.039931 0.19983
## Number of obs: 428, groups: SubjCode, 28
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.468898 0.011560 405.727681 40.564 < 2e-16 ***
## Type2-1 -0.002287 0.023116 405.312514 -0.099 0.921238
## Duration2-1 -0.014526 0.025787 48.991476 -0.563 0.575802
## Resp2-1 -0.004878 0.038157 401.292908 -0.128 0.898334
## Resp3-2 -0.280604 0.031988 386.549431 -8.772 < 2e-16 ***
## Resp4-3 -0.171915 0.030139 388.235257 -5.704 2.32e-08 ***
## Resp5-4 -0.056739 0.037493 399.754139 -1.513 0.130993
## Type2-1:Duration2-1 -0.140723 0.050486 56.564470 -2.787 0.007222 **
## Type2-1:Resp2-1 0.183236 0.076285 400.163399 2.402 0.016761 *
## Type2-1:Resp3-2 0.495156 0.063992 387.233395 7.738 8.88e-14 ***
## Type2-1:Resp4-3 0.277368 0.060278 388.268935 4.601 5.69e-06 ***
## Type2-1:Resp5-4 -0.075327 0.074980 399.423732 -1.005 0.315681
## Duration2-1:Resp2-1 0.272288 0.076320 401.315406 3.568 0.000404 ***
## Duration2-1:Resp3-2 -0.018597 0.063976 386.549431 -0.291 0.771451
## Duration2-1:Resp4-3 -0.190601 0.060278 388.235257 -3.162 0.001690 **
## Duration2-1:Resp5-4 -0.009927 0.075117 402.523833 -0.132 0.894923
## Type2-1:Duration2-1:Resp2-1 0.141611 0.152581 400.136396 0.928 0.353912
## Type2-1:Duration2-1:Resp3-2 -0.075808 0.127985 387.233395 -0.592 0.553982
## Type2-1:Duration2-1:Resp4-3 0.263312 0.120557 388.268935 2.184 0.029550 *
## Type2-1:Duration2-1:Resp5-4 -0.063515 0.150221 402.229698 -0.423 0.672661
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## convergence code: 0
## boundary (singular) fit: see ?isSingularlmm.etd1.resp <- update(lmm.etd.resp,
formula = ratio ~ Type * Duration * Resp +
(0 + Type_Dura | SubjCode))
summary(lmm.etd1.resp)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: ratio ~ Type + Duration + Resp + (0 + Type_Dura | SubjCode) +
## Type:Duration + Type:Resp + Duration:Resp + Type:Duration:Resp
## Data: df.ratio.205
##
## REML criterion at convergence: -83.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9667 -0.2385 -0.0109 0.1084 3.4064
##
## Random effects:
## Groups Name Variance Std.Dev.
## SubjCode Type_Dura 0.00390 0.06245
## Residual 0.04109 0.20270
## Number of obs: 428, groups: SubjCode, 28
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.469474 0.011599 400.842776 40.475 < 2e-16 ***
## Type2-1 -0.001081 0.023203 402.828429 -0.047 0.962865
## Duration2-1 -0.012959 0.023208 404.380853 -0.558 0.576907
## Resp2-1 -0.005719 0.038398 394.875793 -0.149 0.881683
## Resp3-2 -0.280367 0.032431 395.114720 -8.645 < 2e-16 ***
## Resp4-3 -0.169758 0.030528 390.645016 -5.561 4.99e-08 ***
## Resp5-4 -0.057450 0.037761 394.353731 -1.521 0.128954
## Type2-1:Duration2-1 -0.137427 0.047896 44.867934 -2.869 0.006253 **
## Type2-1:Resp2-1 0.181385 0.076846 401.877015 2.360 0.018734 *
## Type2-1:Resp3-2 0.496671 0.064835 389.636392 7.660 1.48e-13 ***
## Type2-1:Resp4-3 0.280445 0.061054 390.264928 4.593 5.89e-06 ***
## Type2-1:Resp5-4 -0.076454 0.075533 396.227120 -1.012 0.312064
## Duration2-1:Resp2-1 0.273275 0.076796 395.007070 3.558 0.000418 ***
## Duration2-1:Resp3-2 -0.018124 0.064862 395.114720 -0.279 0.780062
## Duration2-1:Resp4-3 -0.186286 0.061056 390.645016 -3.051 0.002436 **
## Duration2-1:Resp5-4 -0.006607 0.075536 396.632378 -0.087 0.930339
## Type2-1:Duration2-1:Resp2-1 0.143246 0.153695 402.003852 0.932 0.351887
## Type2-1:Duration2-1:Resp3-2 -0.072778 0.129671 389.636392 -0.561 0.574949
## Type2-1:Duration2-1:Resp4-3 0.269466 0.122109 390.264928 2.207 0.027912 *
## Type2-1:Duration2-1:Resp5-4 -0.056009 0.151094 398.314079 -0.371 0.711067
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need itanova(lmm.etd1.resp, lmm.rdc.resp, refit = FALSE)3.2.1.5 The optimal model
lmm.opt.resp <- lmm.etd1.resp
summary(lmm.opt.resp)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: ratio ~ Type + Duration + Resp + (0 + Type_Dura | SubjCode) +
## Type:Duration + Type:Resp + Duration:Resp + Type:Duration:Resp
## Data: df.ratio.205
##
## REML criterion at convergence: -83.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9667 -0.2385 -0.0109 0.1084 3.4064
##
## Random effects:
## Groups Name Variance Std.Dev.
## SubjCode Type_Dura 0.00390 0.06245
## Residual 0.04109 0.20270
## Number of obs: 428, groups: SubjCode, 28
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.469474 0.011599 400.842776 40.475 < 2e-16 ***
## Type2-1 -0.001081 0.023203 402.828429 -0.047 0.962865
## Duration2-1 -0.012959 0.023208 404.380853 -0.558 0.576907
## Resp2-1 -0.005719 0.038398 394.875793 -0.149 0.881683
## Resp3-2 -0.280367 0.032431 395.114720 -8.645 < 2e-16 ***
## Resp4-3 -0.169758 0.030528 390.645016 -5.561 4.99e-08 ***
## Resp5-4 -0.057450 0.037761 394.353731 -1.521 0.128954
## Type2-1:Duration2-1 -0.137427 0.047896 44.867934 -2.869 0.006253 **
## Type2-1:Resp2-1 0.181385 0.076846 401.877015 2.360 0.018734 *
## Type2-1:Resp3-2 0.496671 0.064835 389.636392 7.660 1.48e-13 ***
## Type2-1:Resp4-3 0.280445 0.061054 390.264928 4.593 5.89e-06 ***
## Type2-1:Resp5-4 -0.076454 0.075533 396.227120 -1.012 0.312064
## Duration2-1:Resp2-1 0.273275 0.076796 395.007070 3.558 0.000418 ***
## Duration2-1:Resp3-2 -0.018124 0.064862 395.114720 -0.279 0.780062
## Duration2-1:Resp4-3 -0.186286 0.061056 390.645016 -3.051 0.002436 **
## Duration2-1:Resp5-4 -0.006607 0.075536 396.632378 -0.087 0.930339
## Type2-1:Duration2-1:Resp2-1 0.143246 0.153695 402.003852 0.932 0.351887
## Type2-1:Duration2-1:Resp3-2 -0.072778 0.129671 389.636392 -0.561 0.574949
## Type2-1:Duration2-1:Resp4-3 0.269466 0.122109 390.264928 2.207 0.027912 *
## Type2-1:Duration2-1:Resp5-4 -0.056009 0.151094 398.314079 -0.371 0.711067
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it3.2.2 Estimated marginal means
emm.resp <- emmeans(lmm.opt.resp, ~ Resp + Duration + Type)
as.data.frame(emm.resp) %>%
select(Type, Duration, Resp , everything())3.2.3 Plots
resp.ColuPlot.205 <- {
ggplot(data = as.data.frame(emm.resp), aes(y = emmean, x = Resp)) + # set the data, varialbes for x and y axises, and the variable for dividing data
geom_col(position = "dodge", color = "black", alpha = .7) + # position of columns and countour of columns
facet_grid(Type ~ Duration,
labeller = labeller(Duration = dura.labels)) +
geom_errorbar(mapping = aes(ymin = lower.CL, ymax = upper.CL), linetype = 1, # set the error bar
show.legend = FALSE, width = 0.25, alpha = .5,
position = position_dodge(width=0.9)) +
geom_hline(yintercept = c(0.5, 1), linetype = 5, alpha = 0.5) + # add the line for 0.5 and 1 (y)
# coord_cartesian(ylim = c(0,1.12) ) + # set the limit for y axis
scale_y_continuous(expand= c(0, 0), limits = c(-0.1, 1.15), breaks = seq(0, 1, 0.25)) + # remove the space between columns and x axis
labs(title = "", x = "Responses", y = "Percentage") + # set the names for main, x and y axises Subjective Responses for E205
scale_fill_grey() + # set the color for columns
# geom_text(label = c("***", "***", "", ""), color = c("red", "red", "red", "red"), size = 6, nudge_y = 0.05, nudge_x = rep(c(-0.225, 0.225), 2)) + # add starts to the significant columns
plot_theme
}
resp.ColuPlot.205
3.2.4 Contrasts
contra.resp <- contrast(emmeans(lmm.opt.resp, ~ Duration | Resp + Type), "pairwise")
contra.resp[c(1, 5)]## contrast Resp Type estimate SE df t.ratio p.value
## 17 - 200 1 intact -0.00760280 0.05449349 373.30 -0.140 0.8891
## 17 - 200 5 intact 0.07210303 0.05499259 374.51 1.311 0.1906
##
## Degrees-of-freedom method: satterthwaiteconfint(contra.resp[c(1, 5)])3.2.5 One-sample t-test
test(emm.resp, null = 0.5)4 ERP amplitude analysis
4.1 Load mean amplitude for single trials
The raw mean amplitude of each trial for all participants were loaded.
# read the trial mean amplitude data
df.erp.E2 <- {
"E205_MeanAmp.csv" %>%
file.path(folder.data, .) %>%
read_csv() %>%
substr_Event() %>%
filter(SubjCode %in% valid.17) %>%
mutate(SubjCode = as.factor(SubjCode),
Hemisphere = as.factor(Hemisphere),
urResponse = as.factor(urResponse),
Stimuli = substr(stimName, 2, 6))
}
df.erp.E2 %>%
group_by(SubjCode, Stimuli, Hemisphere, Type, Category, Duration) %>%
summarize(count = n())4.2 Linear mixed model across responses
4.2.1 Set the successive difference coding
# dummy coding
df.erp.E2.all <- sdif_coding_P203_erp(df.erp.E2)4.2.2 Amplitudes of the P1
# only keep the data for amplitudes of the P1 (correct and incorrect erps)
df.erp.P1.E2 <- {
df.erp.E2.all %>%
filter(Component == "P1")
}
if (saveCSV) {
output.erp.P1.E2 <- file.path(folder.nesi, "E205_erp_P1.RData")
save(df.erp.P1.E2, file = output.erp.P1.E2)
}
# the structure of this dataset
head(df.erp.P1.E2, 10)4.2.2.1 The maximal model
file.max.P1.E2 <- file.path(folder.nesi, "E205_P1_lmm_max.RData")
# fit the maximal model
if (!file.exists(file.max.P1.E2)) {
lmm.max.P1.E2 <- lmer(MeanAmp ~ Type * Category * Duration * Hemisphere +
(1 + Type_C + Cate_C + Dura_C + Hemi_C +
Type_Cate + Type_Dura + Cate_Dura + Type_Hemi + Cate_Hemi + Dura_Hemi +
Type_Cate_Dura + Type_Cate_Hemi + Type_Dura_Hemi + Cate_Dura_Hemi +
Type_Cate_Dura_Hemi
| SubjCode) +
(1 + Type_C + Dura_C + Hemi_C +
Type_Dura + Type_Hemi + Dura_Hemi +
Type_Dura_Hemi
| Stimuli),
data = df.erp.P1.E2,
verbose = TRUE,
control = lmerControl(optimizer = "bobyqa", # nloptwrap Nelder_Mead
optCtrl = list(maxfun = 1e7)))
} else {
load(file.max.P1.E2)
}
print(summary(lmm.max.P1.E2), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Type * Category * Duration * Hemisphere + (1 + Type_C +
## Cate_C + Dura_C + Hemi_C + Type_Cate + Type_Dura + Cate_Dura +
## Type_Hemi + Cate_Hemi + Dura_Hemi + Type_Cate_Dura + Type_Cate_Hemi +
## Type_Dura_Hemi + Cate_Dura_Hemi + Type_Cate_Dura_Hemi | SubjCode) +
## (1 + Type_C + Dura_C + Hemi_C + Type_Dura + Type_Hemi + Dura_Hemi + Type_Dura_Hemi | Stimuli)
## Data: df.erp.P1.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 399168.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.4269 -0.5739 -0.0116 0.5710 9.2481
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## Stimuli (Intercept) 0.020242 0.14228
## Type_C 0.071284 0.26699 0.05
## Dura_C 0.072281 0.26885 0.40 -0.05
## Hemi_C 0.001384 0.03720 0.70 0.19 0.70
## Type_Dura 0.421342 0.64911 -0.10 0.67 0.12 -0.21
## Type_Hemi 0.011305 0.10632 -0.46 0.71 0.02 0.14 0.32
## Dura_Hemi 0.002092 0.04574 0.49 0.68 0.57 0.49 0.73 0.25
## Type_Dura_Hemi 0.075770 0.27526 -0.34 0.77 0.25 0.03 0.79 0.80 0.63
## SubjCode (Intercept) 1.797320 1.34064
## Type_C 0.113610 0.33706 0.10
## Cate_C 0.050124 0.22388 -0.36 -0.29
## Dura_C 0.321549 0.56705 0.44 -0.09 -0.36
## Hemi_C 0.594791 0.77123 0.31 0.11 0.48 -0.18
## Type_Cate 0.378197 0.61498 0.67 0.25 -0.36 0.38 0.10
## Type_Dura 0.336157 0.57979 0.05 0.22 0.05 0.29 0.02 0.41
## Cate_Dura 0.043316 0.20813 0.54 -0.40 0.09 0.18 0.55 0.41 -0.19
## Type_Hemi 0.034810 0.18657 0.02 0.47 -0.17 -0.02 -0.35 -0.27 -0.25 -0.45
## Cate_Hemi 0.011529 0.10737 0.00 0.55 0.23 -0.23 -0.06 0.03 0.17 -0.61 0.51
## Dura_Hemi 0.239585 0.48947 0.19 -0.08 -0.46 0.46 -0.37 0.23 0.19 0.27 0.22 -0.50
## Type_Cate_Dura 0.382269 0.61828 0.70 0.29 -0.67 0.53 -0.34 0.77 0.23 0.03 0.19 0.22 0.32
## Type_Cate_Hemi 0.127146 0.35658 0.00 -0.38 0.04 0.27 0.07 0.52 0.45 0.46 -0.83 -0.52 0.20 0.06
## Type_Dura_Hemi 0.133551 0.36545 -0.48 0.43 0.55 -0.36 0.34 -0.39 0.23 -0.26 0.30 0.29 -0.07 -0.59 -0.26
## Cate_Dura_Hemi 0.074409 0.27278 0.06 -0.44 0.18 0.21 -0.41 -0.16 -0.04 0.05 0.46 0.04 0.50 0.10 -0.18
## Type_Cate_Dura_Hemi 0.470022 0.68558 0.17 -0.64 0.44 0.34 -0.05 0.17 0.28 0.38 -0.16 -0.10 0.30 0.07 0.38
## Residual 15.678168 3.95957
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
## -0.03
## -0.12 0.75
##
## Number of obs: 71282, groups: Stimuli, 80; SubjCode, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.262713 0.245906 29.244440 5.135 1.7e-05 ***
## Type2-1 -0.106661 0.076825 37.967608 -1.388 0.17312
## Category2-1 0.085615 0.062504 46.064875 1.370 0.17741
## Duration2-1 0.353467 0.113340 33.150333 3.119 0.00374 **
## Hemisphere2-1 0.117784 0.145144 29.038601 0.811 0.42368
## Type2-1:Category2-1 0.122340 0.145142 38.767656 0.843 0.40445
## Type2-1:Duration2-1 0.313789 0.146180 44.709815 2.147 0.03729 *
## Category2-1:Duration2-1 0.122743 0.099766 60.821395 1.230 0.22332
## Type2-1:Hemisphere2-1 0.011121 0.078696 72.888288 0.141 0.88801
## Category2-1:Hemisphere2-1 -0.041851 0.073108 178.077600 -0.572 0.56774
## Duration2-1:Hemisphere2-1 0.005918 0.113598 30.243456 0.052 0.95879
## Type2-1:Category2-1:Duration2-1 0.008769 0.231065 63.040636 0.038 0.96985
## Type2-1:Category2-1:Hemisphere2-1 0.190520 0.156105 79.923915 1.220 0.22588
## Type2-1:Duration2-1:Hemisphere2-1 -0.044562 0.158002 76.945281 -0.282 0.77867
## Category2-1:Duration2-1:Hemisphere2-1 -0.151587 0.148829 122.513631 -1.019 0.31043
## Type2-1:Category2-1:Duration2-1:Hemisphere2-1 0.123037 0.312596 84.251373 0.394 0.69487
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## convergence code: 0
## boundary (singular) fit: see ?isSingular4.2.2.2 The zero-correlation-parameter model
file.zcp.P1.E2 <- file.path(folder.nesi, "E205_P1_lmm_zcp.RData")
# fit the zcp model
if (!file.exists(file.zcp.P1.E2)) {
lmm.zcp.P1.E2 <- lmer(MeanAmp ~ Type * Category * Duration * Hemisphere +
(1 + Type_C + Cate_C + Dura_C + Hemi_C +
Type_Cate + Type_Dura + Cate_Dura + Type_Hemi + Cate_Hemi + Dura_Hemi +
Type_Cate_Dura + Type_Cate_Hemi + Type_Dura_Hemi + Cate_Dura_Hemi +
Type_Cate_Dura_Hemi
|| SubjCode) +
(1 + Type_C + Dura_C + Hemi_C +
Type_Dura + Type_Hemi + Dura_Hemi +
Type_Dura_Hemi
|| Stimuli),
data = df.erp.P1.E2,
verbose = TRUE,
control = lmerControl(optimizer = "bobyqa", # nloptwrap Nelder_Mead
optCtrl = list(maxfun = 1e7)))
} else {
load(file.zcp.P1.E2)
}
print(summary(lmm.zcp.P1.E2), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Type * Category * Duration * Hemisphere + (1 + Type_C +
## Cate_C + Dura_C + Hemi_C + Type_Cate + Type_Dura + Cate_Dura +
## Type_Hemi + Cate_Hemi + Dura_Hemi + Type_Cate_Dura + Type_Cate_Hemi +
## Type_Dura_Hemi + Cate_Dura_Hemi + Type_Cate_Dura_Hemi ||
## SubjCode) + (1 + Type_C + Dura_C + Hemi_C + Type_Dura + Type_Hemi + Dura_Hemi + Type_Dura_Hemi || Stimuli)
## Data: df.erp.P1.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 399290.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.5143 -0.5726 -0.0123 0.5716 9.3014
##
## Random effects:
## Groups Name Variance Std.Dev.
## Stimuli Type_Dura_Hemi 1.932e-09 4.395e-05
## Stimuli.1 Dura_Hemi 2.685e-13 5.181e-07
## Stimuli.2 Type_Hemi 4.559e-13 6.752e-07
## Stimuli.3 Type_Dura 2.496e-01 4.996e-01
## Stimuli.4 Hemi_C 0.000e+00 0.000e+00
## Stimuli.5 Dura_C 5.021e-02 2.241e-01
## Stimuli.6 Type_C 3.631e-02 1.906e-01
## Stimuli.7 (Intercept) 1.472e-02 1.213e-01
## SubjCode Type_Cate_Dura_Hemi 3.519e-12 1.876e-06
## SubjCode.1 Cate_Dura_Hemi 1.916e-13 4.377e-07
## SubjCode.2 Type_Dura_Hemi 0.000e+00 0.000e+00
## SubjCode.3 Type_Cate_Hemi 0.000e+00 0.000e+00
## SubjCode.4 Type_Cate_Dura 0.000e+00 0.000e+00
## SubjCode.5 Dura_Hemi 2.525e-01 5.025e-01
## SubjCode.6 Cate_Hemi 0.000e+00 0.000e+00
## SubjCode.7 Type_Hemi 0.000e+00 0.000e+00
## SubjCode.8 Cate_Dura 3.003e-02 1.733e-01
## SubjCode.9 Type_Dura 3.098e-01 5.566e-01
## SubjCode.10 Type_Cate 2.762e-01 5.255e-01
## SubjCode.11 Hemi_C 6.411e-01 8.007e-01
## SubjCode.12 Dura_C 3.127e-01 5.592e-01
## SubjCode.13 Cate_C 4.097e-02 2.024e-01
## SubjCode.14 Type_C 1.055e-01 3.248e-01
## SubjCode.15 (Intercept) 1.781e+00 1.334e+00
## Residual 1.570e+01 3.963e+00
## Number of obs: 71282, groups: Stimuli, 80; SubjCode, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.263e+00 2.446e-01 2.918e+01 5.162 1.59e-05 ***
## Type2-1 -1.068e-01 7.208e-02 3.472e+01 -1.482 0.1474
## Category2-1 8.635e-02 5.768e-02 4.827e+01 1.497 0.1409
## Duration2-1 3.532e-01 1.108e-01 3.198e+01 3.187 0.0032 **
## Hemisphere2-1 1.172e-01 1.503e-01 2.930e+01 0.780 0.4419
## Type2-1:Category2-1 1.227e-01 1.262e-01 4.055e+01 0.972 0.3366
## Type2-1:Duration2-1 3.127e-01 1.355e-01 4.016e+01 2.309 0.0262 *
## Category2-1:Duration2-1 1.223e-01 9.173e-02 4.585e+01 1.333 0.1891
## Type2-1:Hemisphere2-1 1.154e-02 6.999e-02 7.068e+04 0.165 0.8691
## Category2-1:Hemisphere2-1 -4.049e-02 6.999e-02 7.068e+04 -0.579 0.5629
## Duration2-1:Hemisphere2-1 5.761e-03 1.154e-01 3.009e+01 0.050 0.9605
## Type2-1:Category2-1:Duration2-1 7.014e-03 1.791e-01 9.725e+01 0.039 0.9688
## Type2-1:Category2-1:Hemisphere2-1 1.898e-01 1.400e-01 7.068e+04 1.356 0.1752
## Type2-1:Duration2-1:Hemisphere2-1 -4.251e-02 1.400e-01 7.068e+04 -0.304 0.7613
## Category2-1:Duration2-1:Hemisphere2-1 -1.505e-01 1.400e-01 7.068e+04 -1.075 0.2822
## Type2-1:Category2-1:Duration2-1:Hemisphere2-1 1.214e-01 2.800e-01 7.068e+04 0.434 0.6646
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## convergence code: 0
## boundary (singular) fit: see ?isSingular4.2.2.3 The reduced model
summary(rePCA(lmm.zcp.P1.E2))## $Stimuli
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## Standard deviation 0.1261 0.05655 0.04809 0.03062 1.109e-05 1.704e-07 1.308e-07 0
## Proportion of Variance 0.7114 0.14312 0.10350 0.04196 0.000e+00 0.000e+00 0.000e+00 0
## Cumulative Proportion 0.7114 0.85454 0.95804 1.00000 1.000e+00 1.000e+00 1.000e+00 1
##
## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
## Standard deviation 0.3368 0.2021 0.14111 0.14047 0.13262 0.12681 0.08195 0.05108 0.04373 4.734e-07 1.105e-07 0 0
## Proportion of Variance 0.4749 0.1710 0.08339 0.08264 0.07366 0.06734 0.02813 0.01093 0.00801 0.000e+00 0.000e+00 0 0
## Cumulative Proportion 0.4749 0.6459 0.72930 0.81194 0.88559 0.95294 0.98106 0.99199 1.00000 1.000e+00 1.000e+00 1 1
## [,14] [,15] [,16]
## Standard deviation 0 0 0
## Proportion of Variance 0 0 0
## Cumulative Proportion 1 1 1file.rdc.P1.E2 <- file.path(folder.nesi, "E205_P1_lmm_rdc.RData")
# fit the rdc model
if (!file.exists(file.rdc.P1.E2)) {
# lmm.rdc.P1.E2.step <- step(lmm.rdc.P1.E2, reduce.fixed = FALSE)
lmm.rdc.P1.E2 <- update(lmm.zcp.P1.E2,
formula = MeanAmp ~ Type * Category * Duration * Hemisphere +
(1 + Type_C + Cate_C + Dura_C + Hemi_C +
Type_Cate + Type_Dura + Cate_Dura + Dura_Hemi
|| SubjCode) +
(1 + Type_C + Dura_C +
Type_Dura
|| Stimuli))
} else {
load(file.rdc.P1.E2)
}
print(summary(lmm.rdc.P1.E2), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Type + Category + Duration + Hemisphere + (1 + Type_C +
## Cate_C + Dura_C + Hemi_C + Type_Cate + Type_Dura + Cate_Dura +
## Dura_Hemi || SubjCode) + (1 + Type_C + Dura_C + Type_Dura ||
## Stimuli) + Type:Category + Type:Duration + Category:Duration +
## Type:Hemisphere + Category:Hemisphere + Duration:Hemisphere +
## Type:Category:Duration + Type:Category:Hemisphere + Type:Duration:Hemisphere +
## Category:Duration:Hemisphere + Type:Category:Duration:Hemisphere
## Data: df.erp.P1.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 399290.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.5143 -0.5726 -0.0123 0.5716 9.3014
##
## Random effects:
## Groups Name Variance Std.Dev.
## Stimuli Type_Dura 0.24958 0.4996
## Stimuli.1 Dura_C 0.05021 0.2241
## Stimuli.2 Type_C 0.03631 0.1906
## Stimuli.3 (Intercept) 0.01472 0.1213
## SubjCode Dura_Hemi 0.25250 0.5025
## SubjCode.1 Cate_Dura 0.03003 0.1733
## SubjCode.2 Type_Dura 0.30984 0.5566
## SubjCode.3 Type_Cate 0.27617 0.5255
## SubjCode.4 Hemi_C 0.64112 0.8007
## SubjCode.5 Dura_C 0.31265 0.5592
## SubjCode.6 Cate_C 0.04097 0.2024
## SubjCode.7 Type_C 0.10546 0.3248
## SubjCode.8 (Intercept) 1.78060 1.3344
## Residual 15.70241 3.9626
## Number of obs: 71282, groups: Stimuli, 80; SubjCode, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.263e+00 2.446e-01 2.918e+01 5.162 1.59e-05 ***
## Type2-1 -1.068e-01 7.208e-02 3.472e+01 -1.482 0.1474
## Category2-1 8.635e-02 5.768e-02 4.827e+01 1.497 0.1409
## Duration2-1 3.532e-01 1.108e-01 3.198e+01 3.187 0.0032 **
## Hemisphere2-1 1.172e-01 1.503e-01 2.930e+01 0.780 0.4419
## Type2-1:Category2-1 1.227e-01 1.262e-01 4.055e+01 0.972 0.3366
## Type2-1:Duration2-1 3.127e-01 1.355e-01 4.016e+01 2.309 0.0262 *
## Category2-1:Duration2-1 1.223e-01 9.173e-02 4.585e+01 1.333 0.1891
## Type2-1:Hemisphere2-1 1.154e-02 6.999e-02 7.068e+04 0.165 0.8691
## Category2-1:Hemisphere2-1 -4.049e-02 6.999e-02 7.068e+04 -0.579 0.5629
## Duration2-1:Hemisphere2-1 5.761e-03 1.154e-01 3.009e+01 0.050 0.9605
## Type2-1:Category2-1:Duration2-1 7.014e-03 1.791e-01 9.725e+01 0.039 0.9688
## Type2-1:Category2-1:Hemisphere2-1 1.898e-01 1.400e-01 7.068e+04 1.356 0.1752
## Type2-1:Duration2-1:Hemisphere2-1 -4.251e-02 1.400e-01 7.068e+04 -0.304 0.7613
## Category2-1:Duration2-1:Hemisphere2-1 -1.505e-01 1.400e-01 7.068e+04 -1.075 0.2822
## Type2-1:Category2-1:Duration2-1:Hemisphere2-1 1.214e-01 2.800e-01 7.068e+04 0.434 0.6646
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(lmm.rdc.P1.E2, lmm.zcp.P1.E2, refit = FALSE)4.2.2.4 The extended model
file.etd.P1.E2 <- file.path(folder.nesi, "E205_P1_lmm_etd.RData")
# fit the etd model
if (!file.exists(file.etd.P1.E2)) {
lmm.etd.P1.E2 <- update(lmm.rdc.P1.E2,
formula = MeanAmp ~ Type * Category * Duration * Hemisphere +
(1 + Type_C + Cate_C + Dura_C + Hemi_C +
Type_Cate + Type_Dura + Cate_Dura + Dura_Hemi
| SubjCode) +
(1 + Type_C + Dura_C +
Type_Dura
| Stimuli))
lmm.etd.P1.E2.2 <- re_fit(lmm.etd.P1.E2)
} else {
load(file.etd.P1.E2)
}
print(summary(lmm.etd.P1.E2.2), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Type + Category + Duration + Hemisphere + (1 + Type_C +
## Cate_C + Dura_C + Hemi_C + Type_Cate + Type_Dura + Cate_Dura +
## Dura_Hemi | SubjCode) + (1 + Type_C + Dura_C + Type_Dura |
## Stimuli) + Type:Category + Type:Duration + Category:Duration +
## Type:Hemisphere + Category:Hemisphere + Duration:Hemisphere +
## Type:Category:Duration + Type:Category:Hemisphere + Type:Duration:Hemisphere +
## Category:Duration:Hemisphere + Type:Category:Duration:Hemisphere
## Data: df.erp.P1.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 399219.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.4381 -0.5727 -0.0121 0.5713 9.2907
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## Stimuli (Intercept) 0.02013 0.1419
## Type_C 0.06908 0.2628 0.06
## Dura_C 0.07107 0.2666 0.40 -0.06
## Type_Dura 0.41428 0.6436 -0.10 0.67 0.12
## SubjCode (Intercept) 1.79762 1.3408
## Type_C 0.11245 0.3353 0.11
## Cate_C 0.04079 0.2020 -0.35 -0.31
## Dura_C 0.32097 0.5665 0.44 -0.09 -0.37
## Hemi_C 0.61141 0.7819 0.30 0.11 0.50 -0.19
## Type_Cate 0.25555 0.5055 0.62 0.23 -0.23 0.31 0.22
## Type_Dura 0.33290 0.5770 0.05 0.22 0.07 0.29 0.03 0.43
## Cate_Dura 0.04222 0.2055 0.45 -0.45 0.13 0.10 0.58 0.41 -0.23
## Dura_Hemi 0.23709 0.4869 0.22 -0.08 -0.60 0.48 -0.43 0.20 0.16 0.14
## Residual 15.69460 3.9616
## Number of obs: 71282, groups: Stimuli, 80; SubjCode, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.263e+00 2.459e-01 2.924e+01 5.135 1.7e-05 ***
## Type2-1 -1.065e-01 7.640e-02 3.752e+01 -1.394 0.17163
## Category2-1 8.568e-02 5.993e-02 4.572e+01 1.430 0.15958
## Duration2-1 3.540e-01 1.132e-01 3.306e+01 3.127 0.00367 **
## Hemisphere2-1 1.175e-01 1.470e-01 2.908e+01 0.799 0.43066
## Type2-1:Category2-1 1.216e-01 1.299e-01 4.603e+01 0.936 0.35391
## Type2-1:Duration2-1 3.135e-01 1.455e-01 4.433e+01 2.154 0.03669 *
## Category2-1:Duration2-1 1.222e-01 9.930e-02 6.054e+01 1.231 0.22312
## Type2-1:Hemisphere2-1 1.162e-02 6.997e-02 7.073e+04 0.166 0.86805
## Category2-1:Hemisphere2-1 -4.073e-02 6.997e-02 7.073e+04 -0.582 0.56048
## Duration2-1:Hemisphere2-1 6.368e-03 1.131e-01 2.998e+01 0.056 0.95549
## Type2-1:Category2-1:Duration2-1 6.142e-03 2.008e-01 7.769e+01 0.031 0.97567
## Type2-1:Category2-1:Hemisphere2-1 1.886e-01 1.399e-01 7.073e+04 1.348 0.17771
## Type2-1:Duration2-1:Hemisphere2-1 -4.223e-02 1.399e-01 7.073e+04 -0.302 0.76282
## Category2-1:Duration2-1:Hemisphere2-1 -1.508e-01 1.399e-01 7.073e+04 -1.078 0.28117
## Type2-1:Category2-1:Duration2-1:Hemisphere2-1 1.195e-01 2.799e-01 7.073e+04 0.427 0.66937
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(lmm.etd.P1.E2.2, lmm.rdc.P1.E2, refit = FALSE)The reduced model (lmm.rdc.P1.E2) explained the data better than the extended model (lmm.rdc.P1.E2.2).
4.2.2.5 The optimal model
lmm.opt.P1.E205 <- lmm.rdc.P1.E2
# print(summary(lmm.opt.P1.E2), corr = FALSE)
summary(lmm.opt.P1.E205)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Type + Category + Duration + Hemisphere + (1 + Type_C +
## Cate_C + Dura_C + Hemi_C + Type_Cate + Type_Dura + Cate_Dura +
## Dura_Hemi || SubjCode) + (1 + Type_C + Dura_C + Type_Dura ||
## Stimuli) + Type:Category + Type:Duration + Category:Duration +
## Type:Hemisphere + Category:Hemisphere + Duration:Hemisphere +
## Type:Category:Duration + Type:Category:Hemisphere + Type:Duration:Hemisphere +
## Category:Duration:Hemisphere + Type:Category:Duration:Hemisphere
## Data: df.erp.P1.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 399290.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.5143 -0.5726 -0.0123 0.5716 9.3014
##
## Random effects:
## Groups Name Variance Std.Dev.
## Stimuli Type_Dura 0.24958 0.4996
## Stimuli.1 Dura_C 0.05021 0.2241
## Stimuli.2 Type_C 0.03631 0.1906
## Stimuli.3 (Intercept) 0.01472 0.1213
## SubjCode Dura_Hemi 0.25250 0.5025
## SubjCode.1 Cate_Dura 0.03003 0.1733
## SubjCode.2 Type_Dura 0.30984 0.5566
## SubjCode.3 Type_Cate 0.27617 0.5255
## SubjCode.4 Hemi_C 0.64112 0.8007
## SubjCode.5 Dura_C 0.31265 0.5592
## SubjCode.6 Cate_C 0.04097 0.2024
## SubjCode.7 Type_C 0.10546 0.3248
## SubjCode.8 (Intercept) 1.78060 1.3344
## Residual 15.70241 3.9626
## Number of obs: 71282, groups: Stimuli, 80; SubjCode, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.263e+00 2.446e-01 2.918e+01 5.162 1.59e-05 ***
## Type2-1 -1.068e-01 7.208e-02 3.472e+01 -1.482 0.1474
## Category2-1 8.635e-02 5.768e-02 4.827e+01 1.497 0.1409
## Duration2-1 3.532e-01 1.108e-01 3.198e+01 3.187 0.0032 **
## Hemisphere2-1 1.172e-01 1.503e-01 2.930e+01 0.780 0.4419
## Type2-1:Category2-1 1.227e-01 1.262e-01 4.055e+01 0.972 0.3366
## Type2-1:Duration2-1 3.127e-01 1.355e-01 4.016e+01 2.309 0.0262 *
## Category2-1:Duration2-1 1.223e-01 9.173e-02 4.585e+01 1.333 0.1891
## Type2-1:Hemisphere2-1 1.154e-02 6.999e-02 7.068e+04 0.165 0.8691
## Category2-1:Hemisphere2-1 -4.049e-02 6.999e-02 7.068e+04 -0.579 0.5629
## Duration2-1:Hemisphere2-1 5.761e-03 1.154e-01 3.009e+01 0.050 0.9605
## Type2-1:Category2-1:Duration2-1 7.014e-03 1.791e-01 9.725e+01 0.039 0.9688
## Type2-1:Category2-1:Hemisphere2-1 1.898e-01 1.400e-01 7.068e+04 1.356 0.1752
## Type2-1:Duration2-1:Hemisphere2-1 -4.251e-02 1.400e-01 7.068e+04 -0.304 0.7613
## Category2-1:Duration2-1:Hemisphere2-1 -1.505e-01 1.400e-01 7.068e+04 -1.075 0.2822
## Type2-1:Category2-1:Duration2-1:Hemisphere2-1 1.214e-01 2.800e-01 7.068e+04 0.434 0.6646
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 16 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it# confint(lmm.opt.P1.E205, method="Wald")4.2.2.6 Diagnostic plots
# qqplot
qqplot_lmer(lmm.opt.P1.E205)
4.2.2.7 Estimated marginal means
emm.P1.E205 <- emmeans(lmm.opt.P1.E205, ~ Type + Duration + Hemisphere + Category )
emm.P1.E205 %>%
as.data.frame() %>%
select(Type, Category, Duration, Hemisphere, everything())4.2.2.8 Plots
# emmip(emm.P1.E205, Category ~ Duration | Type + Hemisphere, CIs = TRUE)hemiLabel <- c("left", "right")
names(hemiLabel) <- c("Left", "Right")
p1.all.LinePlot = {
ggplot(data = data.frame(emm.P1.E205), aes(y = emmean, x = Duration, color = Category, group = Category)) + # set the data, varialbes for x and y axises, and the variable for dividing data
geom_point(size = 2) +
geom_line(aes(linetype = Category), size = .85) + # position = "dodge", alpha = .7
scale_linetype_manual(values=c("solid", "dashed")) +
geom_errorbar(mapping = aes(ymin = lower.CL, ymax = upper.CL), linetype = 1, # set the error bar
show.legend = FALSE, width = 0.25, alpha = .5) + # , position = position_dodge(width=0.9)
facet_grid(Type ~ Hemisphere, # switch = "x",
labeller = labeller(Hemisphere = hemiLabel)) +
scale_y_reverse(limits = ylimit.p1) + # set the limit for y axis
# scale_y_continuous(expand= c(0, 0)) + # remove the space between columns and x axis
labs(x = "Duration (ms)", y = expression(paste("Amplitudes (", mu, "V)")), fill = "Category") + # set the names for main, x and y axises title = "",
# scale_x_discrete(labels = c("left", "right")) +
# geom_text(label = c("", "", "", "", "***", "", "", ""), color = "red", size = 6, nudge_y = 0.5, nudge_x = 0.5) + # add starts to the significant columns
theme_bw() +
erp_theme
}
# p1.all.LinePlot.slide <- {
# p1.all.LinePlot +
# theme(legend.position="right",
# axis.line = element_line(color="black", size = 3))
# }
# ggsave('P1_all.png', p1.all.LinePlot.slide, width = 7, height = 4)
p1.all.LinePlot
4.2.2.9 Contrasts
contra.all.P1.gen <- contrast(emmeans(lmm.opt.P1.E205, ~ Duration | Hemisphere + Category + Type),
"pairwise", adjust = "Bonferroni")
contra.all.P1.gen[1:2]## contrast Hemisphere Category Type estimate SE df t.ratio p.value
## 17 - 200 Left face intact -0.07107583 0.1643558 122.97 -0.432 1.0000
## 17 - 200 Right face intact -0.20370672 0.1643558 122.97 -1.239 0.4351
##
## Degrees-of-freedom method: satterthwaite
## P value adjustment: bonferroni method for 2 testsconfint(contra.all.P1.gen[1:2])contra.all.P1.spec <- contrast(emmeans(lmm.opt.P1.E205, ~ Category + Duration | Hemisphere + Type),
interaction = "pairwise", adjust = "Bonferroni")
contra.all.P1.spec[1:2]## Category_pairwise Duration_pairwise Hemisphere Type estimate SE df t.ratio p.value
## face - house 17 - 200 Left intact 0.22438787 0.1561692 266.23 1.437 0.3039
## face - house 17 - 200 Right intact 0.01316323 0.1561692 266.23 0.084 1.0000
##
## Degrees-of-freedom method: satterthwaite
## P value adjustment: bonferroni method for 2 testsconfint(contra.all.P1.spec[1:2])contra.all.P1.gen[1:8]## contrast Hemisphere Category Type estimate SE df t.ratio p.value
## 17 - 200 Left face intact -0.0710758 0.1643558 122.97 -0.432 1.0000
## 17 - 200 Right face intact -0.2037067 0.1643558 122.97 -1.239 1.0000
## 17 - 200 Left house intact -0.2954637 0.1646024 123.70 -1.795 0.6007
## 17 - 200 Right house intact -0.2168700 0.1646024 123.70 -1.318 1.0000
## 17 - 200 Left face scrambled -0.4319184 0.1700147 140.79 -2.540 0.0972
## 17 - 200 Right face scrambled -0.4613435 0.1700147 140.79 -2.714 0.0599
## 17 - 200 Left house scrambled -0.6026303 0.1699668 140.64 -3.546 0.0043
## 17 - 200 Right house scrambled -0.5422116 0.1699668 140.64 -3.190 0.0140
##
## Degrees-of-freedom method: satterthwaite
## P value adjustment: bonferroni method for 8 testsconfint(contra.all.P1.gen[1:8])contra.all.P1.spec## Hemisphere = Left, Type = intact:
## Category_pairwise Duration_pairwise estimate SE df t.ratio p.value
## face - house 17 - 200 0.22438787 0.1561692 266.23 1.437 0.1519
##
## Hemisphere = Right, Type = intact:
## Category_pairwise Duration_pairwise estimate SE df t.ratio p.value
## face - house 17 - 200 0.01316323 0.1561692 266.23 0.084 0.9329
##
## Hemisphere = Left, Type = scrambled:
## Category_pairwise Duration_pairwise estimate SE df t.ratio p.value
## face - house 17 - 200 0.17071189 0.1675616 352.74 1.019 0.3090
##
## Hemisphere = Right, Type = scrambled:
## Category_pairwise Duration_pairwise estimate SE df t.ratio p.value
## face - house 17 - 200 0.08086813 0.1675616 352.74 0.483 0.6297
##
## Degrees-of-freedom method: satterthwaite# confint(contra.all.P1.spec[1:4])4.2.3 Ampitudes of the N170
# only keep the data for amplitudes of the N170 (all erps)
df.erp.N170.E2 <- {
df.erp.E2.all %>%
filter(Component == "N170")
}
if (saveCSV) {
output.erp.N170.E2 <- file.path(folder.nesi, "E205_erp_N170.RData")
save(df.erp.N170.E2, file = output.erp.N170.E2)
}
# the structure of this dataset
head(df.erp.N170.E2, 10)4.2.3.1 The maximal model
file.max.N170.E2 <- file.path(folder.nesi, "E205_N170_lmm_max.RData")
# fit the maximal model
if (!file.exists(file.max.N170.E2)) {
lmm.max.N170.E2 <- lmer(MeanAmp ~ Type * Category * Duration * Hemisphere +
(1 + Type_C + Cate_C + Dura_C + Hemi_C +
Type_Cate + Type_Dura + Cate_Dura + Type_Hemi + Cate_Hemi + Dura_Hemi +
Type_Cate_Dura + Type_Cate_Hemi + Type_Dura_Hemi + Cate_Dura_Hemi +
Type_Cate_Dura_Hemi
| SubjCode) +
(1 + Type_C + Dura_C + Hemi_C +
Type_Dura + Type_Hemi + Dura_Hemi +
Type_Dura_Hemi
| Stimuli),
data = df.erp.N170.E2,
verbose = TRUE,
control = lmerControl(optimizer = "bobyqa", # nloptwrap Nelder_Mead
optCtrl = list(maxfun = 1e7)))
} else {
load(file.max.N170.E2)
}
print(summary(lmm.max.N170.E2), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Type * Category * Duration * Hemisphere + (1 + Type_C +
## Cate_C + Dura_C + Hemi_C + Type_Cate + Type_Dura + Cate_Dura +
## Type_Hemi + Cate_Hemi + Dura_Hemi + Type_Cate_Dura + Type_Cate_Hemi +
## Type_Dura_Hemi + Cate_Dura_Hemi + Type_Cate_Dura_Hemi | SubjCode) +
## (1 + Type_C + Dura_C + Hemi_C + Type_Dura + Type_Hemi + Dura_Hemi + Type_Dura_Hemi | Stimuli)
## Data: df.erp.N170.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 401341.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.9374 -0.5879 -0.0046 0.5793 10.5966
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## Stimuli (Intercept) 0.017355 0.1317
## Type_C 0.099516 0.3155 0.23
## Dura_C 0.031794 0.1783 0.62 -0.33
## Hemi_C 0.004382 0.0662 0.32 0.31 -0.14
## Type_Dura 0.375660 0.6129 0.30 0.42 0.13 -0.61
## Type_Hemi 0.002172 0.0466 0.53 0.32 0.11 -0.40 0.89
## Dura_Hemi 0.010231 0.1011 0.49 0.63 0.04 0.87 -0.25 -0.19
## Type_Dura_Hemi 0.040130 0.2003 0.17 -0.51 0.38 -0.76 0.55 0.63 -0.77
## SubjCode (Intercept) 3.125355 1.7679
## Type_C 0.509356 0.7137 -0.23
## Cate_C 0.352785 0.5940 -0.27 0.64
## Dura_C 0.906309 0.9520 0.00 0.20 0.02
## Hemi_C 1.175660 1.0843 -0.21 -0.35 -0.02 -0.34
## Type_Cate 1.443237 1.2013 0.62 -0.57 -0.79 -0.01 -0.06
## Type_Dura 0.838114 0.9155 -0.04 0.59 0.36 -0.03 -0.53 -0.21
## Cate_Dura 0.441519 0.6645 0.21 0.22 0.53 -0.20 -0.16 -0.35 0.62
## Type_Hemi 0.520714 0.7216 -0.03 0.52 0.27 0.24 -0.59 -0.27 0.43 0.21
## Cate_Hemi 0.471528 0.6867 -0.02 0.07 -0.06 -0.01 -0.42 0.02 0.24 0.15 0.83
## Dura_Hemi 1.453583 1.2056 -0.15 0.03 -0.08 0.49 -0.18 0.01 0.17 0.01 0.31 0.26
## Type_Cate_Dura 1.523837 1.2344 0.39 -0.57 -0.70 -0.14 0.22 0.75 -0.62 -0.70 -0.49 -0.30 -0.14
## Type_Cate_Hemi 1.545688 1.2433 0.04 -0.20 0.00 -0.11 0.42 0.08 -0.10 -0.03 -0.86 -0.95 -0.19 0.30
## Type_Dura_Hemi 0.518336 0.7200 0.11 0.71 0.37 -0.08 -0.48 -0.15 0.60 0.21 0.65 0.41 -0.30 -0.34 -0.45
## Cate_Dura_Hemi 0.442789 0.6654 0.11 -0.17 -0.24 -0.43 -0.08 0.21 0.22 0.24 0.48 0.79 -0.08 -0.13 -0.66
## Type_Cate_Dura_Hemi 0.644599 0.8029 0.10 -0.36 0.05 0.29 0.24 0.00 -0.46 -0.11 -0.63 -0.70 -0.02 0.23 0.65
## Residual 16.105886 4.0132
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
## 0.37
## -0.66 -0.78
##
## Number of obs: 71282, groups: Stimuli, 80; SubjCode, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.457986 0.323589 29.121560 -4.506 9.92e-05 ***
## Type2-1 1.035785 0.139571 32.818293 7.421 1.64e-08 ***
## Category2-1 0.953981 0.117833 32.584341 8.096 2.65e-09 ***
## Duration2-1 0.560394 0.178509 29.688145 3.139 0.00381 **
## Hemisphere2-1 0.002074 0.201247 29.015948 0.010 0.99185
## Type2-1:Category2-1 -1.932099 0.241068 34.283041 -8.015 2.29e-09 ***
## Type2-1:Duration2-1 1.014670 0.194070 36.684930 5.228 7.11e-06 ***
## Category2-1:Duration2-1 1.064759 0.146072 32.945035 7.289 2.32e-08 ***
## Type2-1:Hemisphere2-1 0.388565 0.149700 29.423292 2.596 0.01458 *
## Category2-1:Hemisphere2-1 0.188786 0.144784 30.757300 1.304 0.20194
## Duration2-1:Hemisphere2-1 -0.234009 0.231533 29.172466 -1.011 0.32047
## Type2-1:Category2-1:Duration2-1 -2.037947 0.299494 42.540100 -6.805 2.60e-08 ***
## Type2-1:Category2-1:Hemisphere2-1 -0.586785 0.267832 30.765442 -2.191 0.03616 *
## Type2-1:Duration2-1:Hemisphere2-1 0.454275 0.194635 38.826366 2.334 0.02487 *
## Category2-1:Duration2-1:Hemisphere2-1 0.046010 0.188081 41.085375 0.245 0.80796
## Type2-1:Category2-1:Duration2-1:Hemisphere2-1 -0.529665 0.322344 53.344936 -1.643 0.10623
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## convergence code: 0
## boundary (singular) fit: see ?isSingular4.2.3.2 The zero-correlation-parameter model
file.zcp.N170.E2 <- file.path(folder.nesi, "E205_N170_lmm_zcp.RData")
# fit the zcp model
if (!file.exists(file.zcp.N170.E2)) {
lmm.zcp.N170.E2 <- lmer(MeanAmp ~ Type * Category * Duration * Hemisphere +
(1 + Type_C + Cate_C + Dura_C + Hemi_C +
Type_Cate + Type_Dura + Cate_Dura + Type_Hemi + Cate_Hemi + Dura_Hemi +
Type_Cate_Dura + Type_Cate_Hemi + Type_Dura_Hemi + Cate_Dura_Hemi +
Type_Cate_Dura_Hemi
|| SubjCode) +
(1 + Type_C + Dura_C + Hemi_C +
Type_Dura + Type_Hemi + Dura_Hemi +
Type_Dura_Hemi
|| Stimuli),
data = df.erp.N170.E2,
verbose = TRUE,
control = lmerControl(optimizer = "bobyqa", # nloptwrap Nelder_Mead
optCtrl = list(maxfun = 1e7)))
} else {
load(file.zcp.N170.E2)
}
print(summary(lmm.zcp.N170.E2), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Type * Category * Duration * Hemisphere + (1 + Type_C +
## Cate_C + Dura_C + Hemi_C + Type_Cate + Type_Dura + Cate_Dura +
## Type_Hemi + Cate_Hemi + Dura_Hemi + Type_Cate_Dura + Type_Cate_Hemi +
## Type_Dura_Hemi + Cate_Dura_Hemi + Type_Cate_Dura_Hemi ||
## SubjCode) + (1 + Type_C + Dura_C + Hemi_C + Type_Dura + Type_Hemi + Dura_Hemi + Type_Dura_Hemi || Stimuli)
## Data: df.erp.N170.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 401644.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.9367 -0.5852 -0.0038 0.5801 10.5465
##
## Random effects:
## Groups Name Variance Std.Dev.
## Stimuli Type_Dura_Hemi 1.275e-13 3.571e-07
## Stimuli.1 Dura_Hemi 2.294e-14 1.515e-07
## Stimuli.2 Type_Hemi 0.000e+00 0.000e+00
## Stimuli.3 Type_Dura 2.851e-01 5.340e-01
## Stimuli.4 Hemi_C 0.000e+00 0.000e+00
## Stimuli.5 Dura_C 8.721e-03 9.339e-02
## Stimuli.6 Type_C 6.560e-02 2.561e-01
## Stimuli.7 (Intercept) 1.123e-02 1.060e-01
## SubjCode Type_Cate_Dura_Hemi 1.072e-13 3.274e-07
## SubjCode.1 Cate_Dura_Hemi 3.997e-03 6.322e-02
## SubjCode.2 Type_Dura_Hemi 1.168e-01 3.417e-01
## SubjCode.3 Type_Cate_Hemi 1.076e+00 1.037e+00
## SubjCode.4 Type_Cate_Dura 1.287e+00 1.134e+00
## SubjCode.5 Dura_Hemi 1.510e+00 1.229e+00
## SubjCode.6 Cate_Hemi 2.930e-01 5.413e-01
## SubjCode.7 Type_Hemi 3.862e-01 6.215e-01
## SubjCode.8 Cate_Dura 4.000e-01 6.325e-01
## SubjCode.9 Type_Dura 7.758e-01 8.808e-01
## SubjCode.10 Type_Cate 1.289e+00 1.135e+00
## SubjCode.11 Hemi_C 1.186e+00 1.089e+00
## SubjCode.12 Dura_C 9.083e-01 9.530e-01
## SubjCode.13 Cate_C 3.241e-01 5.693e-01
## SubjCode.14 Type_C 4.811e-01 6.936e-01
## SubjCode.15 (Intercept) 3.128e+00 1.769e+00
## Residual 1.614e+01 4.017e+00
## Number of obs: 71282, groups: Stimuli, 80; SubjCode, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.458e+00 3.236e-01 2.908e+01 -4.506 9.93e-05 ***
## Type2-1 1.034e+00 1.346e-01 3.173e+01 7.682 9.88e-09 ***
## Category2-1 9.531e-01 1.124e-01 3.175e+01 8.483 1.15e-09 ***
## Duration2-1 5.603e-01 1.779e-01 2.920e+01 3.150 0.00376 **
## Hemisphere2-1 1.826e-03 2.020e-01 2.904e+01 0.009 0.99285
## Type2-1:Category2-1 -1.930e+00 2.264e-01 3.295e+01 -8.524 7.62e-10 ***
## Type2-1:Duration2-1 1.012e+00 1.856e-01 3.524e+01 5.450 4.02e-06 ***
## Category2-1:Duration2-1 1.063e+00 1.371e-01 2.985e+01 7.748 1.25e-08 ***
## Type2-1:Hemisphere2-1 3.885e-01 1.338e-01 3.142e+01 2.903 0.00671 **
## Category2-1:Hemisphere2-1 1.895e-01 1.217e-01 3.280e+01 1.558 0.12891
## Duration2-1:Hemisphere2-1 -2.345e-01 2.353e-01 2.914e+01 -0.996 0.32721
## Type2-1:Category2-1:Duration2-1 -2.036e+00 2.780e-01 4.017e+01 -7.322 6.54e-09 ***
## Type2-1:Category2-1:Hemisphere2-1 -5.890e-01 2.366e-01 3.446e+01 -2.489 0.01780 *
## Type2-1:Duration2-1:Hemisphere2-1 4.534e-01 1.550e-01 3.135e+01 2.925 0.00636 **
## Category2-1:Duration2-1:Hemisphere2-1 4.961e-02 1.424e-01 3.306e+01 0.348 0.72976
## Type2-1:Category2-1:Duration2-1:Hemisphere2-1 -5.319e-01 2.838e-01 7.052e+04 -1.874 0.06095 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## convergence code: 0
## boundary (singular) fit: see ?isSingular4.2.3.3 The reduced model
summary(rePCA(lmm.zcp.N170.E2))## $Stimuli
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## Standard deviation 0.1329 0.06376 0.02639 0.02325 8.89e-08 3.77e-08 0 0
## Proportion of Variance 0.7692 0.17698 0.03031 0.02353 0.00e+00 0.00e+00 0 0
## Cumulative Proportion 0.7692 0.94617 0.97647 1.00000 1.00e+00 1.00e+00 1 1
##
## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
## Standard deviation 0.4403 0.3059 0.28258 0.28236 0.27113 0.25820 0.23725 0.21927 0.17267 0.15744 0.15471 0.14172 0.13475
## Proportion of Variance 0.2376 0.1147 0.09788 0.09773 0.09011 0.08172 0.06899 0.05893 0.03655 0.03038 0.02934 0.02462 0.02226
## Cumulative Proportion 0.2376 0.3523 0.45020 0.54793 0.63804 0.71975 0.78875 0.84768 0.88423 0.91461 0.94395 0.96857 0.99083
## [,14] [,15] [,16]
## Standard deviation 0.08507 0.01574 8.15e-08
## Proportion of Variance 0.00887 0.00030 0.00e+00
## Cumulative Proportion 0.99970 1.00000 1.00e+00file.rdc.N170.E2 <- file.path(folder.nesi, "E205_N170_lmm_rdc.RData")
# fit the rdc model
if (!file.exists(file.rdc.N170.E2)) {
# lmm.rdc.N170.E2.step <- step(lmm.rdc.N170.E2, reduce.fixed = FALSE)
lmm.rdc.N170.E2 <- update(lmm.zcp.N170.E2,
formula = MeanAmp ~ Type * Category * Duration * Hemisphere +
(1 + Type_C + Cate_C + Dura_C + Hemi_C +
Type_Cate + Type_Dura + Cate_Dura + Type_Hemi + Cate_Hemi + Dura_Hemi +
Type_Cate_Dura + Type_Cate_Hemi + Type_Dura_Hemi
|| SubjCode) +
(1 + Type_C + Dura_C +
Type_Dura
|| Stimuli))
} else {
load(file.rdc.N170.E2)
}
print(summary(lmm.rdc.N170.E2), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Type + Category + Duration + Hemisphere + (1 + Type_C +
## Cate_C + Dura_C + Hemi_C + Type_Cate + Type_Dura + Cate_Dura +
## Type_Hemi + Cate_Hemi + Dura_Hemi + Type_Cate_Dura + Type_Cate_Hemi +
## Type_Dura_Hemi || SubjCode) + (1 + Type_C + Dura_C + Type_Dura ||
## Stimuli) + Type:Category + Type:Duration + Category:Duration +
## Type:Hemisphere + Category:Hemisphere + Duration:Hemisphere +
## Type:Category:Duration + Type:Category:Hemisphere + Type:Duration:Hemisphere +
## Category:Duration:Hemisphere + Type:Category:Duration:Hemisphere
## Data: df.erp.N170.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 401644.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.9367 -0.5852 -0.0037 0.5800 10.5465
##
## Random effects:
## Groups Name Variance Std.Dev.
## Stimuli Type_Dura 0.285125 0.53397
## Stimuli.1 Dura_C 0.008721 0.09339
## Stimuli.2 Type_C 0.065603 0.25613
## Stimuli.3 (Intercept) 0.011234 0.10599
## SubjCode Type_Dura_Hemi 0.116720 0.34164
## SubjCode.1 Type_Cate_Hemi 1.075684 1.03715
## SubjCode.2 Type_Cate_Dura 1.286463 1.13422
## SubjCode.3 Dura_Hemi 1.510497 1.22902
## SubjCode.4 Cate_Hemi 0.292453 0.54079
## SubjCode.5 Type_Hemi 0.386211 0.62146
## SubjCode.6 Cate_Dura 0.399993 0.63245
## SubjCode.7 Type_Dura 0.775818 0.88081
## SubjCode.8 Type_Cate 1.288519 1.13513
## SubjCode.9 Hemi_C 1.186257 1.08915
## SubjCode.10 Dura_C 0.908297 0.95305
## SubjCode.11 Cate_C 0.324088 0.56929
## SubjCode.12 Type_C 0.481105 0.69362
## SubjCode.13 (Intercept) 3.127930 1.76860
## Residual 16.137008 4.01709
## Number of obs: 71282, groups: Stimuli, 80; SubjCode, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.458e+00 3.236e-01 2.908e+01 -4.506 9.93e-05 ***
## Type2-1 1.034e+00 1.346e-01 3.173e+01 7.682 9.88e-09 ***
## Category2-1 9.531e-01 1.124e-01 3.175e+01 8.483 1.15e-09 ***
## Duration2-1 5.603e-01 1.779e-01 2.920e+01 3.150 0.00376 **
## Hemisphere2-1 1.825e-03 2.020e-01 2.904e+01 0.009 0.99285
## Type2-1:Category2-1 -1.930e+00 2.264e-01 3.294e+01 -8.524 7.62e-10 ***
## Type2-1:Duration2-1 1.012e+00 1.856e-01 3.524e+01 5.450 4.02e-06 ***
## Category2-1:Duration2-1 1.063e+00 1.371e-01 2.985e+01 7.748 1.25e-08 ***
## Type2-1:Hemisphere2-1 3.885e-01 1.338e-01 3.143e+01 2.903 0.00671 **
## Category2-1:Hemisphere2-1 1.895e-01 1.216e-01 3.413e+01 1.559 0.12829
## Duration2-1:Hemisphere2-1 -2.345e-01 2.353e-01 2.914e+01 -0.996 0.32722
## Type2-1:Category2-1:Duration2-1 -2.036e+00 2.780e-01 4.017e+01 -7.322 6.54e-09 ***
## Type2-1:Category2-1:Hemisphere2-1 -5.890e-01 2.366e-01 3.446e+01 -2.489 0.01780 *
## Type2-1:Duration2-1:Hemisphere2-1 4.534e-01 1.550e-01 3.135e+01 2.925 0.00636 **
## Category2-1:Duration2-1:Hemisphere2-1 4.963e-02 1.419e-01 7.054e+04 0.350 0.72655
## Type2-1:Category2-1:Duration2-1:Hemisphere2-1 -5.319e-01 2.838e-01 7.054e+04 -1.874 0.06093 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(lmm.rdc.N170.E2, lmm.zcp.N170.E2, refit = FALSE)4.2.3.4 The extended model
file.etd.N170.E2 <- file.path(folder.nesi, "E205_N170_lmm_etd.RData")
# fit the etd model
if (!file.exists(file.etd.N170.E2)) {
lmm.etd.N170.E2 <- update(lmm.rdc.N170.E2,
formula = MeanAmp ~ Type * Category * Duration * Hemisphere +
(1 + Type_C + Cate_C + Dura_C + Hemi_C +
Type_Cate + Type_Dura + Cate_Dura + Type_Hemi + Cate_Hemi + Dura_Hemi +
Type_Cate_Dura + Type_Cate_Hemi + Type_Dura_Hemi
| SubjCode) +
(1 + Type_C + Dura_C +
Type_Dura
| Stimuli))
} else {
load(file.etd.N170.E2)
}
print(summary(lmm.etd.N170.E2), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Type + Category + Duration + Hemisphere + (1 + Type_C +
## Cate_C + Dura_C + Hemi_C + Type_Cate + Type_Dura + Cate_Dura +
## Type_Hemi + Cate_Hemi + Dura_Hemi + Type_Cate_Dura + Type_Cate_Hemi +
## Type_Dura_Hemi | SubjCode) + (1 + Type_C + Dura_C + Type_Dura |
## Stimuli) + Type:Category + Type:Duration + Category:Duration +
## Type:Hemisphere + Category:Hemisphere + Duration:Hemisphere +
## Type:Category:Duration + Type:Category:Hemisphere + Type:Duration:Hemisphere +
## Category:Duration:Hemisphere + Type:Category:Duration:Hemisphere
## Data: df.erp.N170.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 401372.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.9468 -0.5869 -0.0042 0.5788 10.5930
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## Stimuli (Intercept) 0.01700 0.1304
## Type_C 0.09751 0.3123 0.22
## Dura_C 0.03059 0.1749 0.63 -0.36
## Type_Dura 0.36712 0.6059 0.32 0.43 0.12
## SubjCode (Intercept) 3.12552 1.7679
## Type_C 0.50943 0.7137 -0.23
## Cate_C 0.35290 0.5941 -0.27 0.64
## Dura_C 0.90612 0.9519 0.00 0.20 0.02
## Hemi_C 1.17515 1.0840 -0.20 -0.35 -0.02 -0.34
## Type_Cate 1.44334 1.2014 0.62 -0.57 -0.79 -0.01 -0.06
## Type_Dura 0.83973 0.9164 -0.04 0.59 0.36 -0.03 -0.53 -0.21
## Cate_Dura 0.44172 0.6646 0.21 0.21 0.53 -0.20 -0.16 -0.35 0.61
## Type_Hemi 0.51510 0.7177 -0.03 0.53 0.27 0.24 -0.60 -0.28 0.44 0.20
## Cate_Hemi 0.30258 0.5501 -0.05 0.12 0.00 0.12 -0.49 -0.04 0.21 0.11 0.86
## Dura_Hemi 1.45408 1.2059 -0.15 0.03 -0.08 0.49 -0.18 0.01 0.17 0.01 0.31 0.35
## Type_Cate_Dura 1.52322 1.2342 0.39 -0.58 -0.70 -0.14 0.22 0.75 -0.62 -0.70 -0.50 -0.33 -0.14
## Type_Cate_Hemi 1.16352 1.0787 0.04 -0.18 -0.02 -0.22 0.43 0.11 -0.02 0.01 -0.85 -0.95 -0.22 0.29
## Type_Dura_Hemi 0.49472 0.7034 0.11 0.72 0.39 -0.08 -0.49 -0.16 0.61 0.20 0.64 0.37 -0.31 -0.35 -0.37
## Residual 16.11624 4.0145
## Number of obs: 71282, groups: Stimuli, 80; SubjCode, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.458e+00 3.236e-01 2.900e+01 -4.506 9.99e-05 ***
## Type2-1 1.036e+00 1.395e-01 2.389e+01 7.425 1.19e-07 ***
## Category2-1 9.539e-01 1.178e-01 2.705e+01 8.099 1.05e-08 ***
## Duration2-1 5.603e-01 1.785e-01 2.390e+01 3.140 0.00445 **
## Hemisphere2-1 1.499e-03 2.011e-01 1.289e+01 0.007 0.99417
## Type2-1:Category2-1 -1.932e+00 2.409e-01 2.846e+01 -8.021 8.72e-09 ***
## Type2-1:Duration2-1 1.015e+00 1.939e-01 3.399e+01 5.232 8.60e-06 ***
## Category2-1:Duration2-1 1.065e+00 1.459e-01 3.290e+01 7.298 2.29e-08 ***
## Type2-1:Hemisphere2-1 3.889e-01 1.490e-01 2.525e+01 2.610 0.01501 *
## Category2-1:Hemisphere2-1 1.901e-01 1.229e-01 3.634e+01 1.546 0.13075
## Duration2-1:Hemisphere2-1 -2.345e-01 2.313e-01 1.980e+01 -1.014 0.32287
## Type2-1:Category2-1:Duration2-1 -2.038e+00 2.988e-01 3.953e+01 -6.821 3.53e-08 ***
## Type2-1:Category2-1:Hemisphere2-1 -5.894e-01 2.427e-01 3.582e+01 -2.429 0.02031 *
## Type2-1:Duration2-1:Hemisphere2-1 4.561e-01 1.913e-01 3.071e+01 2.384 0.02351 *
## Category2-1:Duration2-1:Hemisphere2-1 4.947e-02 1.418e-01 7.067e+04 0.349 0.72720
## Type2-1:Category2-1:Duration2-1:Hemisphere2-1 -5.344e-01 2.836e-01 7.067e+04 -1.884 0.05956 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## convergence code: 0
## boundary (singular) fit: see ?isSingularsummary(rePCA(lmm.etd.N170.E2))## $Stimuli
## Importance of components:
## [,1] [,2] [,3] [,4]
## Standard deviation 0.1559 0.07239 0.04451 0.01604
## Proportion of Variance 0.7647 0.16489 0.06235 0.00809
## Cumulative Proportion 0.7647 0.92956 0.99191 1.00000
##
## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
## Standard deviation 0.5573 0.4522 0.3638 0.28544 0.23566 0.21812 0.16008 0.13781 0.06854 0.05289 0.0001626 2.121e-06 6.643e-08
## Proportion of Variance 0.3513 0.2313 0.1497 0.09216 0.06282 0.05382 0.02899 0.02148 0.00531 0.00316 0.0000000 0.000e+00 0.000e+00
## Cumulative Proportion 0.3513 0.5826 0.7322 0.82441 0.88724 0.94105 0.97004 0.99152 0.99684 1.00000 1.0000000 1.000e+00 1.000e+00
## [,14]
## Standard deviation 0
## Proportion of Variance 0
## Cumulative Proportion 1file.etd1.N170.E2 <- file.path(folder.nesi, "E205_N170_lmm_etd1.RData")
# fit the etd1 model
if (!file.exists(file.etd1.N170.E2)) {
lmm.etd1.N170.E2 <- update(lmm.etd.N170.E2,
formula = MeanAmp ~ Type * Category * Duration * Hemisphere +
(1 + Type_C + Dura_C + Hemi_C +
Type_Cate + Type_Dura + Type_Hemi + Dura_Hemi +
Type_Cate_Dura + Type_Cate_Hemi
| SubjCode) +
(1 + Type_C + Dura_C +
Type_Dura
| Stimuli))
} else {
load(file.etd1.N170.E2)
}
print(summary(lmm.etd1.N170.E2), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Type + Category + Duration + Hemisphere + (1 + Type_C +
## Dura_C + Hemi_C + Type_Cate + Type_Dura + Type_Hemi + Dura_Hemi +
## Type_Cate_Dura + Type_Cate_Hemi | SubjCode) + (1 + Type_C +
## Dura_C + Type_Dura | Stimuli) + Type:Category + Type:Duration +
## Category:Duration + Type:Hemisphere + Category:Hemisphere +
## Duration:Hemisphere + Type:Category:Duration + Type:Category:Hemisphere +
## Type:Duration:Hemisphere + Category:Duration:Hemisphere + Type:Category:Duration:Hemisphere
## Data: df.erp.N170.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 401774.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.9501 -0.5858 -0.0039 0.5816 10.6037
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## Stimuli (Intercept) 0.01651 0.1285
## Type_C 0.09623 0.3102 0.21
## Dura_C 0.03051 0.1747 0.64 -0.36
## Type_Dura 0.36483 0.6040 0.33 0.42 0.13
## SubjCode (Intercept) 3.12440 1.7676
## Type_C 0.50975 0.7140 -0.23
## Dura_C 0.90542 0.9515 0.00 0.20
## Hemi_C 1.15652 1.0754 -0.21 -0.34 -0.34
## Type_Cate 1.75252 1.3238 0.62 -0.59 -0.03 -0.07
## Type_Dura 0.84363 0.9185 -0.04 0.60 -0.03 -0.53 -0.20
## Type_Hemi 0.38161 0.6177 -0.06 0.42 0.30 -0.56 -0.28 0.34
## Dura_Hemi 1.48018 1.2166 -0.15 0.01 0.49 -0.17 0.03 0.15 0.43
## Type_Cate_Dura 1.31094 1.1450 0.30 -0.44 -0.10 0.25 0.55 -0.66 -0.49 -0.17
## Type_Cate_Hemi 1.69344 1.3013 0.04 -0.18 -0.21 0.46 0.09 -0.05 -0.90 -0.25 0.33
## Residual 16.22266 4.0277
## Number of obs: 71282, groups: Stimuli, 80; SubjCode, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.457e+00 3.235e-01 2.912e+01 -4.504 9.97e-05 ***
## Type2-1 1.035e+00 1.395e-01 3.271e+01 7.417 1.69e-08 ***
## Category2-1 9.523e-01 4.574e-02 7.773e+01 20.822 < 2e-16 ***
## Duration2-1 5.637e-01 1.784e-01 2.968e+01 3.160 0.00362 **
## Hemisphere2-1 2.036e-03 1.995e-01 2.905e+01 0.010 0.99193
## Type2-1:Category2-1 -1.928e+00 2.613e-01 3.344e+01 -7.379 1.64e-08 ***
## Type2-1:Duration2-1 1.017e+00 1.943e-01 3.637e+01 5.235 7.13e-06 ***
## Category2-1:Duration2-1 1.060e+00 8.118e-02 7.754e+01 13.057 < 2e-16 ***
## Type2-1:Hemisphere2-1 3.891e-01 1.333e-01 3.398e+01 2.918 0.00620 **
## Category2-1:Hemisphere2-1 1.909e-01 7.114e-02 7.070e+04 2.683 0.00730 **
## Duration2-1:Hemisphere2-1 -2.350e-01 2.332e-01 2.916e+01 -1.007 0.32199
## Type2-1:Category2-1:Duration2-1 -2.031e+00 2.867e-01 4.327e+01 -7.086 9.37e-09 ***
## Type2-1:Category2-1:Hemisphere2-1 -5.876e-01 2.769e-01 3.445e+01 -2.122 0.04111 *
## Type2-1:Duration2-1:Hemisphere2-1 4.548e-01 1.423e-01 7.070e+04 3.196 0.00139 **
## Category2-1:Duration2-1:Hemisphere2-1 4.671e-02 1.423e-01 7.070e+04 0.328 0.74268
## Type2-1:Category2-1:Duration2-1:Hemisphere2-1 -5.362e-01 2.846e-01 7.070e+04 -1.884 0.05956 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## convergence code: 0
## boundary (singular) fit: see ?isSingularsummary(rePCA(lmm.etd1.N170.E2))## $Stimuli
## Importance of components:
## [,1] [,2] [,3] [,4]
## Standard deviation 0.1548 0.07198 0.04396 0.01573
## Proportion of Variance 0.7650 0.16543 0.06171 0.00790
## Cumulative Proportion 0.7650 0.93039 0.99210 1.00000
##
## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## Standard deviation 0.5368 0.4428 0.3314 0.28204 0.22279 0.21459 0.1596 0.12076 0.04202 6.325e-08
## Proportion of Variance 0.3553 0.2418 0.1354 0.09807 0.06119 0.05677 0.0314 0.01798 0.00218 0.000e+00
## Cumulative Proportion 0.3553 0.5970 0.7324 0.83048 0.89168 0.94845 0.9798 0.99782 1.00000 1.000e+00file.etd2.N170.E2 <- file.path(folder.nesi, "E205_N170_lmm_etd2.RData")
# fit the etd2 model
if (!file.exists(file.etd2.N170.E2)) {
lmm.etd2.N170.E2 <- update(lmm.etd1.N170.E2,
formula = MeanAmp ~ Type * Category * Duration * Hemisphere +
(1 + Type_C + Dura_C + Hemi_C +
Type_Cate + Type_Dura + Dura_Hemi +
Type_Cate_Dura + Type_Cate_Hemi
| SubjCode) +
(1 + Type_C + Dura_C +
Type_Dura
| Stimuli))
} else {
load(file.etd2.N170.E2)
}
print(summary(lmm.etd2.N170.E2), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Type + Category + Duration + Hemisphere + (1 + Type_C +
## Dura_C + Hemi_C + Type_Cate + Type_Dura + Dura_Hemi + Type_Cate_Dura +
## Type_Cate_Hemi | SubjCode) + (1 + Type_C + Dura_C + Type_Dura |
## Stimuli) + Type:Category + Type:Duration + Category:Duration +
## Type:Hemisphere + Category:Hemisphere + Duration:Hemisphere +
## Type:Category:Duration + Type:Category:Hemisphere + Type:Duration:Hemisphere +
## Category:Duration:Hemisphere + Type:Category:Duration:Hemisphere
## Data: df.erp.N170.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 401871.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.9303 -0.5860 -0.0048 0.5817 10.5986
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## Stimuli (Intercept) 0.002805 0.05297
## Type_C 0.092295 0.30380 1.00
## Dura_C 0.011615 0.10777 -0.32 -0.32
## Type_Dura 0.399586 0.63213 0.44 0.44 0.08
## SubjCode (Intercept) 3.123796 1.76743
## Type_C 0.509423 0.71374 -0.23
## Dura_C 0.905459 0.95156 0.00 0.20
## Hemi_C 1.218893 1.10403 -0.20 -0.35 -0.35
## Type_Cate 1.753881 1.32434 0.62 -0.59 -0.03 -0.05
## Type_Dura 0.838924 0.91593 -0.04 0.60 -0.03 -0.53 -0.20
## Dura_Hemi 1.601393 1.26546 -0.15 0.04 0.49 -0.23 0.00 0.17
## Type_Cate_Dura 1.313151 1.14593 0.30 -0.44 -0.10 0.27 0.55 -0.67 -0.21
## Type_Cate_Hemi 1.594705 1.26282 0.04 -0.18 -0.21 0.50 0.09 -0.04 -0.31 0.36
## Residual 16.256515 4.03194
## Number of obs: 71282, groups: Stimuli, 80; SubjCode, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.457e+00 3.232e-01 2.902e+01 -4.507 9.93e-05 ***
## Type2-1 1.034e+00 1.393e-01 3.248e+01 7.425 1.73e-08 ***
## Category2-1 9.525e-01 3.754e-02 4.821e+01 25.376 < 2e-16 ***
## Duration2-1 5.641e-01 1.778e-01 2.926e+01 3.174 0.00353 **
## Hemisphere2-1 2.941e-03 2.047e-01 2.905e+01 0.014 0.98864
## Type2-1:Category2-1 -1.929e+00 2.611e-01 3.316e+01 -7.387 1.69e-08 ***
## Type2-1:Duration2-1 1.016e+00 1.950e-01 3.715e+01 5.208 7.32e-06 ***
## Category2-1:Duration2-1 1.060e+00 7.520e-02 1.147e+02 14.100 < 2e-16 ***
## Type2-1:Hemisphere2-1 3.885e-01 7.122e-02 7.080e+04 5.455 4.92e-08 ***
## Category2-1:Hemisphere2-1 1.916e-01 7.122e-02 7.080e+04 2.690 0.00714 **
## Duration2-1:Hemisphere2-1 -2.349e-01 2.418e-01 2.922e+01 -0.972 0.33917
## Type2-1:Category2-1:Duration2-1 -2.031e+00 2.899e-01 4.504e+01 -7.006 9.93e-09 ***
## Type2-1:Category2-1:Hemisphere2-1 -5.857e-01 2.710e-01 3.421e+01 -2.161 0.03776 *
## Type2-1:Duration2-1:Hemisphere2-1 4.493e-01 1.424e-01 7.080e+04 3.155 0.00161 **
## Category2-1:Duration2-1:Hemisphere2-1 4.771e-02 1.424e-01 7.080e+04 0.335 0.73767
## Type2-1:Category2-1:Duration2-1:Hemisphere2-1 -5.345e-01 2.849e-01 7.080e+04 -1.876 0.06065 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## convergence code: 0
## boundary (singular) fit: see ?isSingularsummary(rePCA(lmm.etd2.N170.E2))## $Stimuli
## Importance of components:
## [,1] [,2] [,3] [,4]
## Standard deviation 0.1611 0.0678 0.02407 1.847e-18
## Proportion of Variance 0.8338 0.1476 0.01861 0.000e+00
## Cumulative Proportion 0.8338 0.9814 1.00000 1.000e+00
##
## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
## Standard deviation 0.5344 0.4352 0.3311 0.26908 0.2225 0.21374 0.15795 0.11781 2.884e-05
## Proportion of Variance 0.3610 0.2394 0.1386 0.09153 0.0626 0.05775 0.03154 0.01755 0.000e+00
## Cumulative Proportion 0.3610 0.6004 0.7390 0.83057 0.8932 0.95092 0.98245 1.00000 1.000e+00file.etd3.N170.E2 <- file.path(folder.nesi, "E205_N170_lmm_etd3.RData")
# fit the etd3 model
if (!file.exists(file.etd3.N170.E2)) {
lmm.etd3.N170.E2 <- update(lmm.etd2.N170.E2,
formula = MeanAmp ~ Type * Category * Duration * Hemisphere +
(1 + Dura_C + Hemi_C +
Type_Cate + Type_Dura + Dura_Hemi +
Type_Cate_Dura + Type_Cate_Hemi
| SubjCode) +
(0 + Type_C + Dura_C +
Type_Dura
| Stimuli))
} else {
load(file.etd3.N170.E2)
}
print(summary(lmm.etd3.N170.E2), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Type + Category + Duration + Hemisphere + (1 + Dura_C +
## Hemi_C + Type_Cate + Type_Dura + Dura_Hemi + Type_Cate_Dura +
## Type_Cate_Hemi | SubjCode) + (0 + Type_C + Dura_C + Type_Dura |
## Stimuli) + Type:Category + Type:Duration + Category:Duration +
## Type:Hemisphere + Category:Hemisphere + Duration:Hemisphere +
## Type:Category:Duration + Type:Category:Hemisphere + Type:Duration:Hemisphere +
## Category:Duration:Hemisphere + Type:Category:Duration:Hemisphere
## Data: df.erp.N170.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 402199
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.9564 -0.5894 -0.0039 0.5825 10.5129
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## Stimuli Type_C 0.09270 0.3045
## Dura_C 0.01805 0.1343 -0.66
## Type_Dura 0.38023 0.6166 0.41 -0.14
## SubjCode (Intercept) 3.14852 1.7744
## Dura_C 0.92884 0.9638 -0.02
## Hemi_C 1.21852 1.1039 -0.19 -0.37
## Type_Cate 1.75483 1.3247 0.63 -0.07 -0.05
## Type_Dura 0.54862 0.7407 0.15 -0.23 -0.34 0.29
## Dura_Hemi 1.60033 1.2650 -0.15 0.49 -0.23 0.00 0.17
## Type_Cate_Dura 1.31892 1.1484 0.30 -0.13 0.27 0.55 -0.43 -0.20
## Type_Cate_Hemi 1.59093 1.2613 0.05 -0.22 0.50 0.09 0.12 -0.31 0.36
## Residual 16.34600 4.0430
## Number of obs: 71282, groups: Stimuli, 80; SubjCode, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.456e+00 3.245e-01 2.900e+01 -4.488 0.000105 ***
## Type2-1 1.031e+00 4.934e-02 7.879e+01 20.905 < 2e-16 ***
## Category2-1 9.524e-01 3.571e-02 7.090e+04 26.665 < 2e-16 ***
## Duration2-1 5.661e-01 1.802e-01 2.942e+01 3.142 0.003812 **
## Hemisphere2-1 2.973e-03 2.047e-01 2.905e+01 0.015 0.988510
## Type2-1:Category2-1 -1.924e+00 2.612e-01 3.328e+01 -7.367 1.76e-08 ***
## Type2-1:Duration2-1 1.011e+00 1.678e-01 4.324e+01 6.025 3.30e-07 ***
## Category2-1:Duration2-1 1.060e+00 7.749e-02 1.154e+02 13.685 < 2e-16 ***
## Type2-1:Hemisphere2-1 3.885e-01 7.142e-02 7.083e+04 5.439 5.36e-08 ***
## Category2-1:Hemisphere2-1 1.916e-01 7.141e-02 7.083e+04 2.683 0.007308 **
## Duration2-1:Hemisphere2-1 -2.349e-01 2.418e-01 2.921e+01 -0.972 0.339161
## Type2-1:Category2-1:Duration2-1 -2.021e+00 2.888e-01 4.417e+01 -7.000 1.12e-08 ***
## Type2-1:Category2-1:Hemisphere2-1 -5.858e-01 2.710e-01 3.424e+01 -2.162 0.037722 *
## Type2-1:Duration2-1:Hemisphere2-1 4.492e-01 1.428e-01 7.083e+04 3.145 0.001661 **
## Category2-1:Duration2-1:Hemisphere2-1 4.772e-02 1.428e-01 7.083e+04 0.334 0.738281
## Type2-1:Category2-1:Duration2-1:Hemisphere2-1 -5.343e-01 2.857e-01 7.083e+04 -1.870 0.061423 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(lmm.etd3.N170.E2, lmm.rdc.N170.E2, refit = FALSE)4.2.3.5 The optimal model
lmm.opt.N170.E205 <- lmm.rdc.N170.E2
print(summary(lmm.opt.N170.E205), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Type + Category + Duration + Hemisphere + (1 + Type_C +
## Cate_C + Dura_C + Hemi_C + Type_Cate + Type_Dura + Cate_Dura +
## Type_Hemi + Cate_Hemi + Dura_Hemi + Type_Cate_Dura + Type_Cate_Hemi +
## Type_Dura_Hemi || SubjCode) + (1 + Type_C + Dura_C + Type_Dura ||
## Stimuli) + Type:Category + Type:Duration + Category:Duration +
## Type:Hemisphere + Category:Hemisphere + Duration:Hemisphere +
## Type:Category:Duration + Type:Category:Hemisphere + Type:Duration:Hemisphere +
## Category:Duration:Hemisphere + Type:Category:Duration:Hemisphere
## Data: df.erp.N170.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 401644.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.9367 -0.5852 -0.0037 0.5800 10.5465
##
## Random effects:
## Groups Name Variance Std.Dev.
## Stimuli Type_Dura 0.285125 0.53397
## Stimuli.1 Dura_C 0.008721 0.09339
## Stimuli.2 Type_C 0.065603 0.25613
## Stimuli.3 (Intercept) 0.011234 0.10599
## SubjCode Type_Dura_Hemi 0.116720 0.34164
## SubjCode.1 Type_Cate_Hemi 1.075684 1.03715
## SubjCode.2 Type_Cate_Dura 1.286463 1.13422
## SubjCode.3 Dura_Hemi 1.510497 1.22902
## SubjCode.4 Cate_Hemi 0.292453 0.54079
## SubjCode.5 Type_Hemi 0.386211 0.62146
## SubjCode.6 Cate_Dura 0.399993 0.63245
## SubjCode.7 Type_Dura 0.775818 0.88081
## SubjCode.8 Type_Cate 1.288519 1.13513
## SubjCode.9 Hemi_C 1.186257 1.08915
## SubjCode.10 Dura_C 0.908297 0.95305
## SubjCode.11 Cate_C 0.324088 0.56929
## SubjCode.12 Type_C 0.481105 0.69362
## SubjCode.13 (Intercept) 3.127930 1.76860
## Residual 16.137008 4.01709
## Number of obs: 71282, groups: Stimuli, 80; SubjCode, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.458e+00 3.236e-01 2.908e+01 -4.506 9.93e-05 ***
## Type2-1 1.034e+00 1.346e-01 3.173e+01 7.682 9.88e-09 ***
## Category2-1 9.531e-01 1.124e-01 3.175e+01 8.483 1.15e-09 ***
## Duration2-1 5.603e-01 1.779e-01 2.920e+01 3.150 0.00376 **
## Hemisphere2-1 1.825e-03 2.020e-01 2.904e+01 0.009 0.99285
## Type2-1:Category2-1 -1.930e+00 2.264e-01 3.294e+01 -8.524 7.62e-10 ***
## Type2-1:Duration2-1 1.012e+00 1.856e-01 3.524e+01 5.450 4.02e-06 ***
## Category2-1:Duration2-1 1.063e+00 1.371e-01 2.985e+01 7.748 1.25e-08 ***
## Type2-1:Hemisphere2-1 3.885e-01 1.338e-01 3.143e+01 2.903 0.00671 **
## Category2-1:Hemisphere2-1 1.895e-01 1.216e-01 3.413e+01 1.559 0.12829
## Duration2-1:Hemisphere2-1 -2.345e-01 2.353e-01 2.914e+01 -0.996 0.32722
## Type2-1:Category2-1:Duration2-1 -2.036e+00 2.780e-01 4.017e+01 -7.322 6.54e-09 ***
## Type2-1:Category2-1:Hemisphere2-1 -5.890e-01 2.366e-01 3.446e+01 -2.489 0.01780 *
## Type2-1:Duration2-1:Hemisphere2-1 4.534e-01 1.550e-01 3.135e+01 2.925 0.00636 **
## Category2-1:Duration2-1:Hemisphere2-1 4.963e-02 1.419e-01 7.054e+04 0.350 0.72655
## Type2-1:Category2-1:Duration2-1:Hemisphere2-1 -5.319e-01 2.838e-01 7.054e+04 -1.874 0.06093 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 14.2.3.6 Diagnostic plots
# qqplot
qqplot_lmer(lmm.opt.N170.E205)
4.2.3.7 Estimated marginal means
emm.N170.E205 <- emmeans(lmm.opt.N170.E205, ~ Duration + Category + Hemisphere + Type)
emm.N170.E205 %>%
as.data.frame() %>%
select(Type, Category, Duration, Hemisphere, everything())4.2.3.8 Plots
# emmip(emm.N170.E205, Category ~ Duration | Type + Hemisphere, CIs = TRUE) +
# scale_y_continuous(trans = "reverse")n170.all.LinePlot = {
ggplot(data = data.frame(emm.N170.E205), aes(y = emmean, x = Duration, color = Category, group = Category)) + # set the data, varialbes for x and y axises, and the variable for dividing data
geom_point(size = 2) +
geom_line(aes(linetype = Category), size = .85) + # position = "dodge", alpha = .7
scale_linetype_manual(values=c("solid", "dashed")) +
geom_errorbar(mapping = aes(ymin = lower.CL, ymax = upper.CL), linetype = 1, # set the error bar
show.legend = FALSE, width = 0.25, alpha = .5) + # , position = position_dodge(width=0.9)
facet_grid(Type ~ Hemisphere, # switch = "x",
labeller = labeller(Hemisphere = hemiLabel)) +
scale_y_reverse(limits = ylimit.n170, breaks = seq(1, -5, -1)) + # set the limit for y axis
# scale_y_continuous(expand= c(0, 0)) + # remove the space between columns and x axis
labs(x = "Duration (ms)", y = expression(paste("Amplitudes (", mu, "V)")), fill = "Category") + # set the names for main, x and y axises title = "",
# scale_x_discrete(labels = c("left", "right")) +
# geom_text(label = c("", "", "", "", "***", "", "", ""), color = "red", size = 6, nudge_y = 0.5, nudge_x = 0.5) + # add starts to the significant columns
theme_bw() +
erp_theme
}
# n170.all.LinePlot.slide <- {
# n170.all.LinePlot +
# scale_y_reverse(limits = c(0.8, -4.8), breaks = seq(0, -4, -1)) + # set the limit for y axis
# theme(legend.position="right",
# axis.line = element_line(color="black", size = 3))
# }
# ggsave('N170_all.png', n170.all.LinePlot.slide, width = 7, height = 4)
n170.all.LinePlot
#### Contrasts
contra.all.N170 <- contrast(emm.N170.E205, "pairwise", simple = "each", combine = TRUE, adjust = "none")
contra.all.N170## Category Hemisphere Type Duration contrast estimate SE df t.ratio p.value
## face Left intact . 17 - 200 0.676342 0.2582103 100.06 2.619 0.0102
## house Left intact . 17 - 200 -1.246277 0.2583720 100.31 -4.824 <.0001
## face Right intact . 17 - 200 1.295381 0.2582103 100.06 5.017 <.0001
## house Right intact . 17 - 200 -0.942840 0.2583720 100.31 -3.649 0.0004
## face Left scrambled . 17 - 200 -0.993654 0.2619497 105.98 -3.793 0.0002
## house Left scrambled . 17 - 200 -1.146543 0.2619183 105.93 -4.377 <.0001
## face Right scrambled . 17 - 200 -1.094031 0.2619497 105.98 -4.176 0.0001
## house Right scrambled . 17 - 200 -1.030586 0.2619183 105.93 -3.935 0.0001
## . Left intact 17 face - house -0.714902 0.1954714 116.95 -3.657 0.0004
## . Left intact 200 face - house -2.637521 0.2219773 194.49 -11.882 <.0001
## . Right intact 17 face - house -1.041124 0.1954714 116.95 -5.326 <.0001
## . Right intact 200 face - house -3.279344 0.2219773 194.49 -14.773 <.0001
## . Left scrambled 17 face - house 0.035906 0.2046084 140.40 0.175 0.8609
## . Left scrambled 200 face - house -0.116982 0.2222826 195.56 -0.526 0.5993
## . Right scrambled 17 face - house 0.032712 0.2046084 140.40 0.160 0.8732
## . Right scrambled 200 face - house 0.096157 0.2222826 195.56 0.433 0.6658
## face . intact 17 Left - Right 0.124897 0.2527268 62.97 0.494 0.6229
## face . intact 200 Left - Right 0.743936 0.2735580 86.44 2.719 0.0079
## house . intact 17 Left - Right -0.201325 0.2526993 62.95 -0.797 0.4286
## house . intact 200 Left - Right 0.102112 0.2738871 86.86 0.373 0.7102
## face . scrambled 17 Left - Right -0.198354 0.2598409 70.37 -0.763 0.4478
## face . scrambled 200 Left - Right -0.298730 0.2739983 87.00 -1.090 0.2786
## house . scrambled 17 Left - Right -0.201548 0.2598447 70.37 -0.776 0.4406
## house . scrambled 200 Left - Right -0.085591 0.2739377 86.92 -0.312 0.7554
## face Left . 17 intact - scrambled -0.822638 0.2246563 126.17 -3.662 0.0004
## face Left . 200 intact - scrambled -2.492634 0.2444100 176.75 -10.199 <.0001
## house Left . 17 intact - scrambled -0.071830 0.2246428 126.14 -0.320 0.7497
## house Left . 200 intact - scrambled 0.027904 0.2445602 177.18 0.114 0.9093
## face Right . 17 intact - scrambled -1.145889 0.2246563 126.17 -5.101 <.0001
## face Right . 200 intact - scrambled -3.535300 0.2444100 176.75 -14.465 <.0001
## house Right . 17 intact - scrambled -0.072053 0.2246428 126.14 -0.321 0.7489
## house Right . 200 intact - scrambled -0.159799 0.2445602 177.18 -0.653 0.5143
##
## Degrees-of-freedom method: satterthwaiteconfint(contra.all.N170)contra.all.N170.gen <- contrast(emmeans(lmm.opt.N170.E205, ~ Duration | Hemisphere + Category + Type),
"pairwise")
contra.all.N170.gen[1:2]## contrast Hemisphere Category Type estimate SE df t.ratio p.value
## 17 - 200 Left face intact 0.6763417 0.2582103 100.06 2.619 0.0102
## 17 - 200 Right face intact 1.2953806 0.2582103 100.06 5.017 <.0001
##
## Degrees-of-freedom method: satterthwaiteconfint(contra.all.N170.gen[1:2])contra.all.N170.gen.hemi <- contrast(emmeans(lmm.opt.N170.E205, ~ Duration + Hemisphere |Category + Type),
interaction = "pairwise")
contra.all.N170.gen.hemi[1]## Duration_pairwise Hemisphere_pairwise Category Type estimate SE df t.ratio p.value
## 17 - 200 Left - Right face intact -0.6190389 0.2636081 45.58 -2.348 0.0233
##
## Degrees-of-freedom method: satterthwaiteconfint(contra.all.N170.gen.hemi[1])contra.all.N170.spec <- contrast(emmeans(lmm.opt.N170.E205, ~ Category + Duration | Hemisphere + Type),
interaction = "pairwise", adjust = "Bonferroni")
contra.all.N170.spec[c(1,2)]## Category_pairwise Duration_pairwise Hemisphere Type estimate SE df t.ratio p.value
## face - house 17 - 200 Left intact 1.922618 0.2151955 104.11 8.934 <.0001
## face - house 17 - 200 Right intact 2.238221 0.2151955 104.11 10.401 <.0001
##
## Degrees-of-freedom method: satterthwaite
## P value adjustment: bonferroni method for 2 testsconfint(contra.all.N170.spec[c(1,2)])contra.all.N170.spec.hemi <- contrast(emmeans(lmm.opt.N170.E205, ~ Category + Duration + Hemisphere | Type),
interaction = "pairwise")
contra.all.N170.spec.hemi## Type = intact:
## Category_pairwise Duration_pairwise Hemisphere_pairwise estimate SE df t.ratio p.value
## face - house 17 - 200 Left - Right -0.3156022 0.1910371 70540.74 -1.652 0.0985
##
## Type = scrambled:
## Category_pairwise Duration_pairwise Hemisphere_pairwise estimate SE df t.ratio p.value
## face - house 17 - 200 Left - Right 0.2163333 0.2099393 70546.20 1.030 0.3028
##
## Degrees-of-freedom method: satterthwaiteconfint(contra.all.N170.spec[c(1,2)])contra.all.N170.gen[1:8]## contrast Hemisphere Category Type estimate SE df t.ratio p.value
## 17 - 200 Left face intact 0.6763417 0.2582103 100.06 2.619 0.1614
## 17 - 200 Right face intact 1.2953806 0.2582103 100.06 5.017 0.0001
## 17 - 200 Left house intact -1.2462767 0.2583720 100.31 -4.824 0.0001
## 17 - 200 Right house intact -0.9428400 0.2583720 100.31 -3.649 0.0096
## 17 - 200 Left face scrambled -0.9936544 0.2619497 105.98 -3.793 0.0059
## 17 - 200 Right face scrambled -1.0940310 0.2619497 105.98 -4.176 0.0015
## 17 - 200 Left house scrambled -1.1465426 0.2619183 105.93 -4.377 0.0007
## 17 - 200 Right house scrambled -1.0305859 0.2619183 105.93 -3.935 0.0036
##
## Degrees-of-freedom method: satterthwaite
## P value adjustment: tukey method for comparing a family of 8 estimatesconfint(contra.all.N170.gen[1:8])contra.all.N170.gen.hemi[1:4]## Duration_pairwise Hemisphere_pairwise Category Type estimate SE df t.ratio p.value
## 17 - 200 Left - Right face intact -0.6190389 0.2636081 45.58 -2.348 0.0233
## 17 - 200 Left - Right house intact -0.3034367 0.2639238 45.80 -1.150 0.2562
## 17 - 200 Left - Right face scrambled 0.1003766 0.2708823 50.83 0.371 0.7125
## 17 - 200 Left - Right house scrambled -0.1159567 0.2708231 50.78 -0.428 0.6703
##
## Degrees-of-freedom method: satterthwaiteconfint(contra.all.N170.gen.hemi[1:4])contra.all.N170.spec## Hemisphere = Left, Type = intact:
## Category_pairwise Duration_pairwise estimate SE df t.ratio p.value
## face - house 17 - 200 1.9226184 0.2151955 104.11 8.934 <.0001
##
## Hemisphere = Right, Type = intact:
## Category_pairwise Duration_pairwise estimate SE df t.ratio p.value
## face - house 17 - 200 2.2382207 0.2151955 104.11 10.401 <.0001
##
## Hemisphere = Left, Type = scrambled:
## Category_pairwise Duration_pairwise estimate SE df t.ratio p.value
## face - house 17 - 200 0.1528882 0.2238302 121.85 0.683 0.4959
##
## Hemisphere = Right, Type = scrambled:
## Category_pairwise Duration_pairwise estimate SE df t.ratio p.value
## face - house 17 - 200 -0.0634451 0.2238302 121.85 -0.283 0.7773
##
## Degrees-of-freedom method: satterthwaite# contra.all.N170.spec.hemi4.3 Linear mixed model with subjective confidence
4.3.1 Converting response to subjective confidence
df.erp.SC.E2 <- {
df.erp.E2 %>%
filter(Type == "intact") %>% # only keep the intact trials
mutate(Confidence = if_else(Response %in% c("11", "55"), "high",
if_else(Response %in% c("12", "54"), "low",
if_else(substring(Response, 2) == "3", "guess", "NA"))),
DuraConf = paste(Duration, Confidence, sep = "_")) %>%
filter(Confidence != "NA",
!(DuraConf %in% c("200_guess", "200_low"))) %>%
mutate(Confidence = as.factor(Confidence),
DuraConf = as.factor(DuraConf))
}4.3.2 Set the successive difference coding
# successive difference coding
df.erp.SC.E2 <- sdif_coding_E205_erp(df.erp.SC.E2)4.3.3 Amplitudes of the P1
# only keep the data for amplitudes of the P1 (correct and incorrect erps)
df.erp.P1.SC.E2 <- {
df.erp.SC.E2 %>%
filter(Component == "P1")
}
if (saveCSV) {
output.erp.P1.SC.E2 <- file.path(folder.nesi, "E205_erp_P1_SC.RData")
save(df.erp.P1.SC.E2, file = output.erp.P1.SC.E2)
}
# the structure of this dataset
head(df.erp.P1.SC.E2, 10)4.3.3.1 The maximal model
lmm.max.P1.SC.E2 <- lmer(MeanAmp ~ Category * Hemisphere * DuraConf +
(1 + Cate_C + Hemi_C + Cate_Hemi | SubjCode) +
(1 + Hemi_C | Stimuli),
data = df.erp.P1.SC.E2,
verbose = TRUE,
control = lmerControl(optimizer = "bobyqa")) # nloptwrap Nelder_Mead## start par. = 1 0 1 1 0 0 0 1 0 0 1 0 1 fn = 221091.6
## At return
## eval: 1276 fn: 220102.61 par: 0.0378072 0.00819981 0.00000 0.338147 -0.0678826 0.0508097 -0.000956547 0.0773424 0.0621324 0.0254029 0.214512 -0.0111323 2.21822e-06## boundary (singular) fit: see ?isSingularprint(summary(lmm.max.P1.SC.E2), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Category * Hemisphere * DuraConf + (1 + Cate_C + Hemi_C + Cate_Hemi | SubjCode) + (1 + Hemi_C | Stimuli)
## Data: df.erp.P1.SC.E2
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 220102.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.6020 -0.5787 -0.0155 0.5775 8.4272
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## Stimuli (Intercept) 0.021529 0.14673
## Hemi_C 0.001013 0.03182 1.00
## SubjCode (Intercept) 1.722174 1.31232
## Cate_C 0.159499 0.39937 -0.66
## Hemi_C 0.790084 0.88887 0.22 0.06
## Cate_Hemi 0.011600 0.10770 -0.03 0.71 -0.14
## Residual 15.061410 3.88090
## Number of obs: 39600, groups: Stimuli, 80; SubjCode, 28
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.248e+00 2.495e-01 2.730e+01 5.002 2.94e-05 ***
## Category2-1 5.822e-02 9.297e-02 3.683e+01 0.626 0.5350
## Hemisphere2-1 5.166e-02 1.733e-01 2.748e+01 0.298 0.7678
## DuraConf2-1 1.051e-01 6.125e-02 2.983e+04 1.716 0.0862 .
## DuraConf3-2 2.647e-01 6.564e-02 1.776e+04 4.033 5.54e-05 ***
## DuraConf4-3 3.971e-02 6.696e-02 2.288e+04 0.593 0.5532
## Category2-1:Hemisphere2-1 -9.480e-02 8.859e-02 2.180e+02 -1.070 0.2857
## Category2-1:DuraConf2-1 -2.347e-01 1.209e-01 1.212e+04 -1.941 0.0523 .
## Category2-1:DuraConf3-2 3.841e-02 1.263e-01 2.598e+03 0.304 0.7612
## Category2-1:DuraConf4-3 5.656e-02 1.312e-01 6.594e+03 0.431 0.6663
## Hemisphere2-1:DuraConf2-1 1.840e-02 1.205e-01 3.585e+04 0.153 0.8786
## Hemisphere2-1:DuraConf3-2 7.275e-02 1.272e-01 2.436e+04 0.572 0.5673
## Hemisphere2-1:DuraConf4-3 5.993e-02 1.314e-01 2.522e+04 0.456 0.6483
## Category2-1:Hemisphere2-1:DuraConf2-1 1.322e-01 2.334e-01 2.924e+04 0.566 0.5713
## Category2-1:Hemisphere2-1:DuraConf3-2 -1.058e-01 2.330e-01 1.268e+04 -0.454 0.6498
## Category2-1:Hemisphere2-1:DuraConf4-3 -1.807e-01 2.513e-01 2.114e+04 -0.719 0.4721
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## convergence code: 0
## boundary (singular) fit: see ?isSingular4.3.3.2 The zero-correlation-parameter model
lmm.zcp.P1.SC.E2 <- update(lmm.max.P1.SC.E2,
formula = MeanAmp ~ Category * Hemisphere * DuraConf +
(1 + Cate_C + Hemi_C + Cate_Hemi || SubjCode) +
(1 + Hemi_C || Stimuli))## start par. = 1 1 1 1 1 1 fn = 221091.6
## At return
## eval: 509 fn: 220118.27 par: 6.22133e-08 0.0375916 1.47841e-06 0.229002 0.102779 0.338213## boundary (singular) fit: see ?isSingularprint(summary(lmm.zcp.P1.SC.E2), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Category + Hemisphere + DuraConf + (1 + Cate_C + Hemi_C +
## Cate_Hemi || SubjCode) + (1 + Hemi_C || Stimuli) + Category:Hemisphere +
## Category:DuraConf + Hemisphere:DuraConf + Category:Hemisphere:DuraConf
## Data: df.erp.P1.SC.E2
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 220118.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.604 -0.578 -0.015 0.578 8.456
##
## Random effects:
## Groups Name Variance Std.Dev.
## Stimuli Hemi_C 5.830e-14 2.415e-07
## Stimuli.1 (Intercept) 2.129e-02 1.459e-01
## SubjCode Cate_Hemi 3.292e-11 5.738e-06
## SubjCode.1 Hemi_C 7.899e-01 8.888e-01
## SubjCode.2 Cate_C 1.591e-01 3.989e-01
## SubjCode.3 (Intercept) 1.723e+00 1.313e+00
## Residual 1.506e+01 3.881e+00
## Number of obs: 39600, groups: Stimuli, 80; SubjCode, 28
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.249e+00 2.495e-01 2.730e+01 5.007 2.91e-05 ***
## Category2-1 6.073e-02 9.286e-02 3.677e+01 0.654 0.5172
## Hemisphere2-1 5.095e-02 1.732e-01 2.746e+01 0.294 0.7709
## DuraConf2-1 1.039e-01 6.148e-02 3.568e+04 1.690 0.0911 .
## DuraConf3-2 2.724e-01 6.610e-02 2.887e+04 4.121 3.78e-05 ***
## DuraConf4-3 3.293e-02 6.739e-02 3.365e+04 0.489 0.6251
## Category2-1:Hemisphere2-1 -9.713e-02 8.593e-02 3.917e+04 -1.130 0.2584
## Category2-1:DuraConf2-1 -2.413e-01 1.220e-01 2.111e+04 -1.979 0.0479 *
## Category2-1:DuraConf3-2 6.113e-02 1.290e-01 5.815e+03 0.474 0.6357
## Category2-1:DuraConf4-3 4.158e-02 1.330e-01 1.365e+04 0.313 0.7546
## Hemisphere2-1:DuraConf2-1 2.186e-02 1.205e-01 3.789e+04 0.181 0.8561
## Hemisphere2-1:DuraConf3-2 6.451e-02 1.272e-01 2.990e+04 0.507 0.6121
## Hemisphere2-1:DuraConf4-3 6.541e-02 1.315e-01 3.598e+04 0.497 0.6189
## Category2-1:Hemisphere2-1:DuraConf2-1 1.270e-01 2.332e-01 3.936e+04 0.545 0.5860
## Category2-1:Hemisphere2-1:DuraConf3-2 -1.051e-01 2.325e-01 3.908e+04 -0.452 0.6513
## Category2-1:Hemisphere2-1:DuraConf4-3 -1.768e-01 2.512e-01 3.933e+04 -0.704 0.4814
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## convergence code: 0
## boundary (singular) fit: see ?isSingular4.3.3.3 The reduced model
summary(rePCA(lmm.zcp.P1.SC.E2))## $Stimuli
## Importance of components:
## [,1] [,2]
## Standard deviation 0.03759 6.221e-08
## Proportion of Variance 1.00000 0.000e+00
## Cumulative Proportion 1.00000 1.000e+00
##
## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4]
## Standard deviation 0.3382 0.2290 0.10278 1.478e-06
## Proportion of Variance 0.6448 0.2956 0.05955 0.000e+00
## Cumulative Proportion 0.6448 0.9405 1.00000 1.000e+00lmm.rdc.P1.SC.E2 <- update(lmm.zcp.P1.SC.E2,
formula = MeanAmp ~ Category * Hemisphere * DuraConf +
(1 + Cate_C + Hemi_C || SubjCode) +
(1 | Stimuli))## start par. = 1 1 1 1 fn = 220659.7
## At return
## eval: 243 fn: 220118.27 par: 0.0375916 0.229002 0.102779 0.338212print(summary(lmm.rdc.P1.SC.E2), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Category + Hemisphere + DuraConf + (1 + Cate_C + Hemi_C ||
## SubjCode) + (1 | Stimuli) + Category:Hemisphere + Category:DuraConf + Hemisphere:DuraConf + Category:Hemisphere:DuraConf
## Data: df.erp.P1.SC.E2
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 220118.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.604 -0.578 -0.015 0.578 8.456
##
## Random effects:
## Groups Name Variance Std.Dev.
## Stimuli (Intercept) 0.02129 0.1459
## SubjCode Hemi_C 0.78991 0.8888
## SubjCode.1 Cate_C 0.15911 0.3989
## SubjCode.2 (Intercept) 1.72297 1.3126
## Residual 15.06265 3.8811
## Number of obs: 39600, groups: Stimuli, 80; SubjCode, 28
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.249e+00 2.495e-01 2.730e+01 5.007 2.91e-05 ***
## Category2-1 6.073e-02 9.286e-02 3.677e+01 0.654 0.5172
## Hemisphere2-1 5.095e-02 1.732e-01 2.746e+01 0.294 0.7709
## DuraConf2-1 1.039e-01 6.148e-02 3.568e+04 1.690 0.0911 .
## DuraConf3-2 2.724e-01 6.610e-02 2.887e+04 4.121 3.78e-05 ***
## DuraConf4-3 3.293e-02 6.739e-02 3.365e+04 0.489 0.6251
## Category2-1:Hemisphere2-1 -9.713e-02 8.593e-02 3.917e+04 -1.130 0.2584
## Category2-1:DuraConf2-1 -2.413e-01 1.220e-01 2.111e+04 -1.979 0.0479 *
## Category2-1:DuraConf3-2 6.113e-02 1.290e-01 5.815e+03 0.474 0.6357
## Category2-1:DuraConf4-3 4.158e-02 1.330e-01 1.365e+04 0.313 0.7546
## Hemisphere2-1:DuraConf2-1 2.186e-02 1.205e-01 3.789e+04 0.181 0.8561
## Hemisphere2-1:DuraConf3-2 6.451e-02 1.272e-01 2.990e+04 0.507 0.6121
## Hemisphere2-1:DuraConf4-3 6.541e-02 1.315e-01 3.598e+04 0.497 0.6189
## Category2-1:Hemisphere2-1:DuraConf2-1 1.270e-01 2.332e-01 3.936e+04 0.545 0.5860
## Category2-1:Hemisphere2-1:DuraConf3-2 -1.051e-01 2.325e-01 3.908e+04 -0.452 0.6513
## Category2-1:Hemisphere2-1:DuraConf4-3 -1.768e-01 2.512e-01 3.933e+04 -0.704 0.4814
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(lmm.rdc.P1.SC.E2, lmm.zcp.P1.SC.E2, refit = FALSE)4.3.3.4 The extended model
lmm.etd.P1.SC.E2 <- update(lmm.rdc.P1.SC.E2,
formula = MeanAmp ~ Category * Hemisphere * DuraConf +
(1 + Cate_C + Hemi_C | SubjCode) +
(1 | Stimuli))## start par. = 1 1 0 0 1 0 1 fn = 220659.7
## At return
## eval: 531 fn: 220104.36 par: 0.0376818 0.338066 -0.0679951 0.0508008 0.0772716 0.0621638 0.214439print(summary(lmm.etd.P1.SC.E2), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Category + Hemisphere + DuraConf + (1 + Cate_C + Hemi_C |
## SubjCode) + (1 | Stimuli) + Category:Hemisphere + Category:DuraConf + Hemisphere:DuraConf + Category:Hemisphere:DuraConf
## Data: df.erp.P1.SC.E2
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 220104.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.6075 -0.5789 -0.0158 0.5770 8.4361
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## Stimuli (Intercept) 0.02139 0.1462
## SubjCode (Intercept) 1.72148 1.3121
## Cate_C 0.15958 0.3995 -0.66
## Hemi_C 0.78972 0.8887 0.22 0.06
## Residual 15.06256 3.8811
## Number of obs: 39600, groups: Stimuli, 80; SubjCode, 28
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.247e+00 2.494e-01 2.730e+01 5.001 2.95e-05 ***
## Category2-1 5.783e-02 9.295e-02 3.674e+01 0.622 0.5377
## Hemisphere2-1 5.142e-02 1.732e-01 2.746e+01 0.297 0.7688
## DuraConf2-1 1.059e-01 6.127e-02 3.012e+04 1.728 0.0840 .
## DuraConf3-2 2.622e-01 6.568e-02 1.817e+04 3.992 6.58e-05 ***
## DuraConf4-3 4.173e-02 6.698e-02 2.308e+04 0.623 0.5333
## Category2-1:Hemisphere2-1 -9.602e-02 8.592e-02 3.911e+04 -1.118 0.2638
## Category2-1:DuraConf2-1 -2.375e-01 1.210e-01 1.240e+04 -1.962 0.0497 *
## Category2-1:DuraConf3-2 3.344e-02 1.265e-01 2.649e+03 0.264 0.7916
## Category2-1:DuraConf4-3 6.136e-02 1.313e-01 6.668e+03 0.467 0.6402
## Hemisphere2-1:DuraConf2-1 2.104e-02 1.205e-01 3.756e+04 0.175 0.8614
## Hemisphere2-1:DuraConf3-2 6.857e-02 1.271e-01 2.857e+04 0.539 0.5896
## Hemisphere2-1:DuraConf4-3 6.261e-02 1.314e-01 3.537e+04 0.476 0.6338
## Category2-1:Hemisphere2-1:DuraConf2-1 1.247e-01 2.332e-01 3.933e+04 0.535 0.5928
## Category2-1:Hemisphere2-1:DuraConf3-2 -9.808e-02 2.324e-01 3.898e+04 -0.422 0.6731
## Category2-1:Hemisphere2-1:DuraConf4-3 -1.824e-01 2.511e-01 3.930e+04 -0.726 0.4677
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(lmm.rdc.P1.SC.E2, lmm.etd.P1.SC.E2, refit = FALSE)4.3.3.5 The optimal model
lmm.opt.P1.SC.E205 <- lmm.etd.P1.SC.E2
# print(summary(lmm.opt.P1.SC.E2), corr = FALSE)
summary(lmm.opt.P1.SC.E205)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Category + Hemisphere + DuraConf + (1 + Cate_C + Hemi_C |
## SubjCode) + (1 | Stimuli) + Category:Hemisphere + Category:DuraConf + Hemisphere:DuraConf + Category:Hemisphere:DuraConf
## Data: df.erp.P1.SC.E2
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 220104.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.6075 -0.5789 -0.0158 0.5770 8.4361
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## Stimuli (Intercept) 0.02139 0.1462
## SubjCode (Intercept) 1.72148 1.3121
## Cate_C 0.15958 0.3995 -0.66
## Hemi_C 0.78972 0.8887 0.22 0.06
## Residual 15.06256 3.8811
## Number of obs: 39600, groups: Stimuli, 80; SubjCode, 28
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.247e+00 2.494e-01 2.730e+01 5.001 2.95e-05 ***
## Category2-1 5.783e-02 9.295e-02 3.674e+01 0.622 0.5377
## Hemisphere2-1 5.142e-02 1.732e-01 2.746e+01 0.297 0.7688
## DuraConf2-1 1.059e-01 6.127e-02 3.012e+04 1.728 0.0840 .
## DuraConf3-2 2.622e-01 6.568e-02 1.817e+04 3.992 6.58e-05 ***
## DuraConf4-3 4.173e-02 6.698e-02 2.308e+04 0.623 0.5333
## Category2-1:Hemisphere2-1 -9.602e-02 8.592e-02 3.911e+04 -1.118 0.2638
## Category2-1:DuraConf2-1 -2.375e-01 1.210e-01 1.240e+04 -1.962 0.0497 *
## Category2-1:DuraConf3-2 3.344e-02 1.265e-01 2.649e+03 0.264 0.7916
## Category2-1:DuraConf4-3 6.136e-02 1.313e-01 6.668e+03 0.467 0.6402
## Hemisphere2-1:DuraConf2-1 2.104e-02 1.205e-01 3.756e+04 0.175 0.8614
## Hemisphere2-1:DuraConf3-2 6.857e-02 1.271e-01 2.857e+04 0.539 0.5896
## Hemisphere2-1:DuraConf4-3 6.261e-02 1.314e-01 3.537e+04 0.476 0.6338
## Category2-1:Hemisphere2-1:DuraConf2-1 1.247e-01 2.332e-01 3.933e+04 0.535 0.5928
## Category2-1:Hemisphere2-1:DuraConf3-2 -9.808e-02 2.324e-01 3.898e+04 -0.422 0.6731
## Category2-1:Hemisphere2-1:DuraConf4-3 -1.824e-01 2.511e-01 3.930e+04 -0.726 0.4677
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 16 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need itconfint(lmm.opt.P1.SC.E205, method = "Wald")## 2.5 % 97.5 %
## .sig01 NA NA
## .sig02 NA NA
## .sig03 NA NA
## .sig04 NA NA
## .sig05 NA NA
## .sig06 NA NA
## .sig07 NA NA
## .sigma NA NA
## (Intercept) 0.75856142 1.7362659230
## Category2-1 -0.12434217 0.2399977383
## Hemisphere2-1 -0.28803158 0.3908789061
## DuraConf2-1 -0.01421216 0.2259624238
## DuraConf3-2 0.13345668 0.3909005188
## DuraConf4-3 -0.08955721 0.1730168953
## Category2-1:Hemisphere2-1 -0.26442374 0.0723833894
## Category2-1:DuraConf2-1 -0.47465631 -0.0003052446
## Category2-1:DuraConf3-2 -0.21453862 0.2814168415
## Category2-1:DuraConf4-3 -0.19592559 0.3186523455
## Hemisphere2-1:DuraConf2-1 -0.21510182 0.2571774938
## Hemisphere2-1:DuraConf3-2 -0.18060402 0.3177447840
## Hemisphere2-1:DuraConf4-3 -0.19499835 0.3202179117
## Category2-1:Hemisphere2-1:DuraConf2-1 -0.33231516 0.5816903695
## Category2-1:Hemisphere2-1:DuraConf3-2 -0.55367829 0.3575264802
## Category2-1:Hemisphere2-1:DuraConf4-3 -0.67462443 0.3098548097anova(lmm.opt.P1.SC.E205)4.3.3.6 Diagnostic plots
qqplot_lmer(lmm.opt.P1.SC.E205)
4.3.3.7 Estimated marginal means
emm.P1.SC.E205 <- emmeans(lmm.opt.P1.SC.E205, ~ DuraConf + Category | Hemisphere)
emm.P1.SC.E205## Hemisphere = Left:
## DuraConf Category emmean SE df lower.CL upper.CL
## 17_guess face 0.9079673 0.2947474 36.58 0.3105221 1.505413
## 17_low face 1.1532358 0.2832600 31.22 0.5756860 1.730786
## 17_high face 1.3398907 0.2802008 29.89 0.7675549 1.912227
## 200_high face 1.2740378 0.2854063 32.19 0.6928164 1.855259
## 17_guess house 1.1592962 0.2318241 32.23 0.6872180 1.631374
## 17_low house 1.1047401 0.2309510 31.76 0.6341687 1.575311
## 17_high house 1.3738720 0.2507271 43.94 0.8685443 1.879200
## 200_high house 1.4605749 0.2369209 35.19 0.9796916 1.941458
##
## Hemisphere = Right:
## DuraConf Category emmean SE df lower.CL upper.CL
## 17_guess face 0.9411258 0.3233773 34.61 0.2843680 1.597884
## 17_low face 1.1450884 0.3129860 30.38 0.5062175 1.783959
## 17_high face 1.4493516 0.3102156 29.31 0.8151845 2.083519
## 200_high face 1.5373008 0.3149735 31.16 0.8950445 2.179557
## 17_guess house 1.0975529 0.2701625 30.76 0.5463754 1.648731
## 17_low house 1.1263785 0.2693767 30.41 0.5765456 1.676211
## 17_high house 1.4150429 0.2868544 39.02 0.8348323 1.995253
## 200_high house 1.4731631 0.2744723 32.78 0.9146025 2.031724
##
## Degrees-of-freedom method: satterthwaite
## Confidence level used: 0.954.3.3.8 Plots
# emmip(emm.P1.SC.E205, Category ~ DuraConf | Hemisphere, CIs = TRUE)duraconf.labels = c("17ms\nguess", "17ms\nlow", "17ms\nhigh", "200ms\nhigh")
p1.conf.LinePlot = {
ggplot(data = data.frame(emm.P1.SC.E205), aes(y = emmean, x = DuraConf, color = Category, group = Category)) + # set the data, varialbes for x and y axises, and the variable for dividing data
geom_point(size = 2) +
geom_line(aes(linetype = Category), size = .85) + # position = "dodge", alpha = .7
scale_linetype_manual(values=c("solid", "dashed")) +
geom_errorbar(mapping = aes(ymin = lower.CL, ymax = upper.CL), linetype = 1, # set the error bar
show.legend = FALSE, width = 0.25, alpha = .5) + # , position = position_dodge(width=0.9)
facet_grid(. ~ Hemisphere, # switch = "x",
labeller = labeller(Hemisphere = hemiLabel)) +
scale_y_reverse(limits = ylimit.p1) + # set the limit for y axis
scale_x_discrete(labels = duraconf.labels) +
# scale_y_continuous(expand= c(0, 0)) + # remove the space between columns and x axis
labs(x = "Duration and Subjective Confidence", y = expression(paste("Amplitudes (", mu, "V)")), fill = "Category") + # set the names for main, x and y axises title = "",
# scale_x_discrete(labels = c("left", "right")) +
# geom_text(label = c("", "", "", "", "***", "", "", ""), color = "red", size = 6, nudge_y = 0.5, nudge_x = 0.5) + # add starts to the significant columns
theme_bw() +
erp_theme
}
# p1.conf.LinePlot.slide <- {
# p1.conf.LinePlot +
# theme(legend.position="right",
# axis.line = element_line(color="black", size = 3))
# }
# ggsave('P1_conf.png', p1.conf.LinePlot.slide, width = 8.5, height = 4)
p1.conf.LinePlot
4.3.3.9 Contrast
# contra.SC.P1 <- contrast(emmeans(lmm.opt.P1.SC.E205, ~ DuraConf + Category), "pairwise", simple = "each", combine = TRUE)
#
# contra.SC.P1
# confint(contra.SC.P1)contra.SC.P1.gen <- contrast(emmeans(lmm.opt.P1.SC.E205, ~ DuraConf | Hemisphere + Category), #
"pairwise", adjust = "Bonferroni")
contra.SC.P1.gen[1:6]## contrast Hemisphere Category estimate SE df t.ratio p.value
## 17_guess - 17_low Left face -0.2452685 0.1354348 37115.10 -1.811 0.4209
## 17_guess - 17_high Left face -0.4319234 0.1376417 20687.47 -3.138 0.0102
## 17_guess - 200_high Left face -0.3660704 0.1425063 36491.93 -2.569 0.0613
## 17_low - 17_high Left face -0.1866549 0.1094711 17691.77 -1.705 0.5292
## 17_low - 200_high Left face -0.1208019 0.1171315 37690.70 -1.031 1.0000
## 17_high - 200_high Left face 0.0658529 0.1097015 36231.42 0.600 1.0000
##
## Degrees-of-freedom method: satterthwaite
## P value adjustment: bonferroni method for 6 testsconfint(contra.SC.P1.gen[1:6])contra.SC.P1.gen[7:12]## contrast Hemisphere Category estimate SE df t.ratio p.value
## 17_guess - 17_low Right face -0.2039625 0.1352749 35346.58 -1.508 0.7897
## 17_guess - 17_high Right face -0.5082257 0.1370905 15779.38 -3.707 0.0013
## 17_guess - 200_high Right face -0.5961750 0.1423218 34614.66 -4.189 0.0002
## 17_low - 17_high Right face -0.3042632 0.1089672 13017.90 -2.792 0.0315
## 17_low - 200_high Right face -0.3922125 0.1170154 36493.11 -3.352 0.0048
## 17_high - 200_high Right face -0.0879492 0.1095568 34348.22 -0.803 1.0000
##
## Degrees-of-freedom method: satterthwaite
## P value adjustment: bonferroni method for 6 testsconfint(contra.SC.P1.gen[7:12])# differences between hemispheres
contra.SC.P1.gen.hemi <- contrast(emmeans(lmm.opt.P1.SC.E205, ~ DuraConf + Hemisphere | Category), #
interaction = "pairwise", adjust = "Bonferroni")
contra.SC.P1.gen.hemi## Category = face:
## DuraConf_pairwise Hemisphere_pairwise estimate SE df t.ratio p.value
## 17_guess - 17_low Left - Right -0.04130597 0.1894667 39164.20 -0.218 1.0000
## 17_guess - 17_high Left - Right 0.07630237 0.1884018 34438.11 0.405 1.0000
## 17_guess - 200_high Left - Right 0.23010456 0.1994051 39057.60 1.154 1.0000
## 17_low - 17_high Left - Right 0.11760834 0.1490986 33092.64 0.789 1.0000
## 17_low - 200_high Left - Right 0.27141052 0.1643000 39274.39 1.652 0.5914
## 17_high - 200_high Left - Right 0.15380219 0.1535298 39052.83 1.002 1.0000
##
## Category = house:
## DuraConf_pairwise Hemisphere_pairwise estimate SE df t.ratio p.value
## 17_guess - 17_low Left - Right 0.08338164 0.1425409 37230.92 0.585 1.0000
## 17_guess - 17_high Left - Right 0.10291407 0.1998271 30438.15 0.515 1.0000
## 17_guess - 200_high Left - Right 0.07433145 0.1591297 39289.66 0.467 1.0000
## 17_low - 17_high Left - Right 0.01953243 0.1926455 35036.26 0.101 1.0000
## 17_low - 200_high Left - Right -0.00905019 0.1571648 39426.36 -0.058 1.0000
## 17_high - 200_high Left - Right -0.02858262 0.2061952 36837.59 -0.139 1.0000
##
## Degrees-of-freedom method: satterthwaite
## P value adjustment: bonferroni method for 6 testscontra.SC.P1.gen.nohemi <- contrast(emmeans(lmm.opt.P1.SC.E205, ~ DuraConf | Category), #
"pairwise", adjust = "Bonferroni")## NOTE: Results may be misleading due to involvement in interactionscontra.SC.P1.gen.nohemi[1:6]## contrast Category estimate SE df t.ratio p.value
## 17_guess - 17_low face -0.2246155 0.09667744 31481.61 -2.323 0.1210
## 17_guess - 17_high face -0.4700746 0.09997852 9957.84 -4.702 <.0001
## 17_guess - 200_high face -0.4811227 0.10169157 30213.22 -4.731 <.0001
## 17_low - 17_high face -0.2454590 0.07982037 7934.70 -3.075 0.0127
## 17_low - 200_high face -0.2565072 0.08341207 33288.82 -3.075 0.0126
## 17_high - 200_high face -0.0110482 0.07826687 29623.74 -0.141 1.0000
##
## Results are averaged over the levels of: Hemisphere
## Degrees-of-freedom method: satterthwaite
## P value adjustment: bonferroni method for 6 testsconfint(contra.SC.P1.gen.nohemi[1:6])contra.SC.P1.spec <- contrast(emmeans(lmm.opt.P1.SC.E205, ~ Category + DuraConf | Hemisphere), #
interaction = "pairwise", adjust = "Bonferroni")
contra.SC.P1.spec## Hemisphere = Left:
## Category_pairwise DuraConf_pairwise estimate SE df t.ratio p.value
## face - house 17_guess - 17_low -0.29982458 0.1679815 24749.21 -1.785 0.4458
## face - house 17_guess - 17_high -0.21734752 0.1920037 5076.78 -1.132 1.0000
## face - house 17_guess - 200_high -0.06479173 0.1800928 31977.14 -0.360 1.0000
## face - house 17_low - 17_high 0.08247706 0.1716760 7492.40 0.480 1.0000
## face - house 17_low - 200_high 0.23503285 0.1617098 38100.50 1.453 0.8767
## face - house 17_high - 200_high 0.15255578 0.1814463 15301.15 0.841 1.0000
##
## Hemisphere = Right:
## Category_pairwise DuraConf_pairwise estimate SE df t.ratio p.value
## face - house 17_guess - 17_low -0.17513697 0.1680863 25502.54 -1.042 1.0000
## face - house 17_guess - 17_high -0.19073582 0.1923894 5541.48 -0.991 1.0000
## face - house 17_guess - 200_high -0.22056484 0.1800024 31001.30 -1.225 1.0000
## face - house 17_low - 17_high -0.01559884 0.1719311 8082.12 -0.091 1.0000
## face - house 17_low - 200_high -0.04542787 0.1616989 37871.30 -0.281 1.0000
## face - house 17_high - 200_high -0.02982903 0.1818779 17940.90 -0.164 1.0000
##
## Degrees-of-freedom method: satterthwaite
## P value adjustment: bonferroni method for 6 testsconfint(contra.SC.P1.spec)contra.SC.P1.spec.hemi <- contrast(emmeans(lmm.opt.P1.SC.E205, ~ Category + DuraConf + Hemisphere ), #
interaction = "pairwise", adjust = "Bonferroni")
contra.SC.P1.spec.hemi## Category_pairwise DuraConf_pairwise Hemisphere_pairwise estimate SE df t.ratio p.value
## face - house 17_guess - 17_low Left - Right -0.1246876 0.2331690 39329.59 -0.535 1.0000
## face - house 17_guess - 17_high Left - Right -0.0266117 0.2573193 39170.26 -0.103 1.0000
## face - house 17_guess - 200_high Left - Right 0.1557731 0.2514099 39451.95 0.620 1.0000
## face - house 17_low - 17_high Left - Right 0.0980759 0.2324545 38979.93 0.422 1.0000
## face - house 17_low - 200_high Left - Right 0.2804607 0.2272674 39363.21 1.234 1.0000
## face - house 17_high - 200_high Left - Right 0.1823848 0.2511473 39297.25 0.726 1.0000
##
## Degrees-of-freedom method: satterthwaite
## P value adjustment: bonferroni method for 6 tests4.3.4 Ampitudes of the N170
# only keep the data for amplitudes of the N170 (all erps)
df.erp.N170.SC.E2 <- {
df.erp.SC.E2 %>%
filter(Component == "N170")
}
if (saveCSV) {
output.erp.N170.SC.E2 <- file.path(folder.nesi, "E205_erp_N170_SC.RData")
save(df.erp.N170.SC.E2, file = output.erp.N170.SC.E2)
}
# the structure of this dataset
head(df.erp.N170.SC.E2, 10)4.3.4.1 The maximal model
lmm.max.N170.SC.E2 <- lmer(MeanAmp ~ Category * Hemisphere * DuraConf +
(1 + Cate_C + Hemi_C + Cate_Hemi | SubjCode) +
(1 + Hemi_C | Stimuli),
data = df.erp.N170.SC.E2,
verbose = TRUE,
control = lmerControl(optimizer = "bobyqa")) # nloptwrap Nelder_Mead## start par. = 1 0 1 1 0 0 0 1 0 0 1 0 1 fn = 222383.5
## At return
## eval: 1493 fn: 221500.89 par: 0.0377450 0.00592574 1.37979e-07 0.468918 -0.124988 -0.00676750 -0.0181408 0.172581 -0.0131746 -0.0558577 0.351848 -0.213332 0.184848## boundary (singular) fit: see ?isSingularprint(summary(lmm.max.N170.SC.E2), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Category * Hemisphere * DuraConf + (1 + Cate_C + Hemi_C + Cate_Hemi | SubjCode) + (1 + Hemi_C | Stimuli)
## Data: df.erp.N170.SC.E2
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 221500.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.3165 -0.5946 0.0000 0.5895 6.7424
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## Stimuli (Intercept) 2.217e-02 0.14888
## Hemi_C 5.463e-04 0.02337 1.00
## SubjCode (Intercept) 3.421e+00 1.84957
## Cate_C 7.064e-01 0.84049 -0.59
## Hemi_C 1.929e+00 1.38904 -0.02 -0.02
## Cate_Hemi 1.293e+00 1.13723 -0.06 -0.12 -0.73
## Residual 1.556e+01 3.94434
## Number of obs: 39600, groups: Stimuli, 80; SubjCode, 28
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.996e+00 3.506e-01 2.714e+01 -5.693 4.68e-06 ***
## Category2-1 1.217e+00 1.682e-01 2.946e+01 7.236 5.24e-08 ***
## Hemisphere2-1 -1.775e-01 2.661e-01 2.726e+01 -0.667 0.51039
## DuraConf2-1 -4.935e-01 6.263e-02 3.817e+04 -7.880 3.35e-15 ***
## DuraConf3-2 -1.805e-01 6.747e-02 3.568e+04 -2.675 0.00747 **
## DuraConf4-3 3.715e-01 6.865e-02 3.638e+04 5.412 6.29e-08 ***
## Category2-1:Hemisphere2-1 3.189e-01 2.322e-01 2.856e+01 1.373 0.18036
## Category2-1:DuraConf2-1 5.042e-01 1.248e-01 3.307e+04 4.039 5.37e-05 ***
## Category2-1:DuraConf3-2 3.517e-01 1.335e-01 1.716e+04 2.635 0.00843 **
## Category2-1:DuraConf4-3 1.770e+00 1.365e-01 2.594e+04 12.971 < 2e-16 ***
## Hemisphere2-1:DuraConf2-1 -1.560e-03 1.242e-01 3.044e+04 -0.013 0.98998
## Hemisphere2-1:DuraConf3-2 -2.377e-01 1.329e-01 1.699e+04 -1.789 0.07358 .
## Hemisphere2-1:DuraConf4-3 -1.685e-01 1.354e-01 1.950e+04 -1.244 0.21353
## Category2-1:Hemisphere2-1:DuraConf2-1 2.736e-01 2.465e-01 1.737e+04 1.110 0.26707
## Category2-1:Hemisphere2-1:DuraConf3-2 -5.786e-02 2.597e-01 4.298e+03 -0.223 0.82373
## Category2-1:Hemisphere2-1:DuraConf4-3 2.674e-01 2.675e-01 8.591e+03 1.000 0.31755
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## convergence code: 0
## boundary (singular) fit: see ?isSingular4.3.4.2 The zero-correlation-parameter model
lmm.zcp.N170.SC.E2 <- update(lmm.max.N170.SC.E2,
formula = MeanAmp ~ Category * Hemisphere * DuraConf +
(1 + Cate_C + Hemi_C + Cate_Hemi || SubjCode) +
(1 + Hemi_C || Stimuli))## start par. = 1 1 1 1 1 1 fn = 222383.5
## At return
## eval: 564 fn: 221530.50 par: 0.00000 0.0376425 0.288393 0.352222 0.212488 0.469156## boundary (singular) fit: see ?isSingularprint(summary(lmm.zcp.N170.SC.E2), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Category + Hemisphere + DuraConf + (1 + Cate_C + Hemi_C +
## Cate_Hemi || SubjCode) + (1 + Hemi_C || Stimuli) + Category:Hemisphere +
## Category:DuraConf + Hemisphere:DuraConf + Category:Hemisphere:DuraConf
## Data: df.erp.N170.SC.E2
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 221530.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.3076 -0.5935 -0.0001 0.5902 6.7081
##
## Random effects:
## Groups Name Variance Std.Dev.
## Stimuli Hemi_C 0.00000 0.0000
## Stimuli.1 (Intercept) 0.02205 0.1485
## SubjCode Cate_Hemi 1.29398 1.1375
## SubjCode.1 Hemi_C 1.93014 1.3893
## SubjCode.2 Cate_C 0.70246 0.8381
## SubjCode.3 (Intercept) 3.42446 1.8505
## Residual 15.55810 3.9444
## Number of obs: 39600, groups: Stimuli, 80; SubjCode, 28
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.995e+00 3.508e-01 2.714e+01 -5.687 4.76e-06 ***
## Category2-1 1.217e+00 1.677e-01 2.946e+01 7.253 5.00e-08 ***
## Hemisphere2-1 -1.757e-01 2.662e-01 2.726e+01 -0.660 0.51480
## DuraConf2-1 -4.958e-01 6.270e-02 3.886e+04 -7.909 2.67e-15 ***
## DuraConf3-2 -1.785e-01 6.761e-02 3.821e+04 -2.640 0.00829 **
## DuraConf4-3 3.698e-01 6.879e-02 3.855e+04 5.376 7.66e-08 ***
## Category2-1:Hemisphere2-1 3.189e-01 2.323e-01 2.856e+01 1.373 0.18044
## Category2-1:DuraConf2-1 5.018e-01 1.251e-01 3.672e+04 4.011 6.05e-05 ***
## Category2-1:DuraConf3-2 3.702e-01 1.343e-01 2.615e+04 2.757 0.00584 **
## Category2-1:DuraConf4-3 1.759e+00 1.371e-01 3.347e+04 12.835 < 2e-16 ***
## Hemisphere2-1:DuraConf2-1 3.713e-03 1.248e-01 3.817e+04 0.030 0.97626
## Hemisphere2-1:DuraConf3-2 -2.309e-01 1.342e-01 3.377e+04 -1.721 0.08521 .
## Hemisphere2-1:DuraConf4-3 -1.775e-01 1.368e-01 3.704e+04 -1.298 0.19430
## Category2-1:Hemisphere2-1:DuraConf2-1 2.960e-01 2.486e-01 3.101e+04 1.191 0.23377
## Category2-1:Hemisphere2-1:DuraConf3-2 -3.908e-02 2.652e-01 1.341e+04 -0.147 0.88287
## Category2-1:Hemisphere2-1:DuraConf4-3 2.456e-01 2.718e-01 2.410e+04 0.904 0.36614
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## convergence code: 0
## boundary (singular) fit: see ?isSingularanova(lmm.max.N170.SC.E2, lmm.zcp.N170.SC.E2, refit = FALSE)4.3.4.3 The reduced model
summary(rePCA(lmm.zcp.N170.SC.E2))## $Stimuli
## Importance of components:
## [,1] [,2]
## Standard deviation 0.03764 0
## Proportion of Variance 1.00000 0
## Cumulative Proportion 1.00000 1
##
## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4]
## Standard deviation 0.4692 0.3522 0.2884 0.21249
## Proportion of Variance 0.4658 0.2626 0.1760 0.09556
## Cumulative Proportion 0.4658 0.7284 0.9044 1.00000lmm.rdc.N170.SC.E2 <- update(lmm.zcp.N170.SC.E2,
formula = MeanAmp ~ Category * Hemisphere * DuraConf +
(1 + Cate_C + Hemi_C + Cate_Hemi || SubjCode) +
(1 | Stimuli))## start par. = 1 1 1 1 1 fn = 222042.5
## At return
## eval: 456 fn: 221530.50 par: 0.0376425 0.288392 0.352219 0.212488 0.469155print(summary(lmm.rdc.N170.SC.E2), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Category + Hemisphere + DuraConf + (1 + Cate_C + Hemi_C +
## Cate_Hemi || SubjCode) + (1 | Stimuli) + Category:Hemisphere +
## Category:DuraConf + Hemisphere:DuraConf + Category:Hemisphere:DuraConf
## Data: df.erp.N170.SC.E2
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 221530.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.3076 -0.5935 -0.0001 0.5902 6.7081
##
## Random effects:
## Groups Name Variance Std.Dev.
## Stimuli (Intercept) 0.02205 0.1485
## SubjCode Cate_Hemi 1.29397 1.1375
## SubjCode.1 Hemi_C 1.93011 1.3893
## SubjCode.2 Cate_C 0.70247 0.8381
## SubjCode.3 (Intercept) 3.42443 1.8505
## Residual 15.55810 3.9444
## Number of obs: 39600, groups: Stimuli, 80; SubjCode, 28
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.995e+00 3.508e-01 2.714e+01 -5.687 4.75e-06 ***
## Category2-1 1.217e+00 1.677e-01 2.946e+01 7.253 5.00e-08 ***
## Hemisphere2-1 -1.757e-01 2.662e-01 2.726e+01 -0.660 0.51479
## DuraConf2-1 -4.958e-01 6.270e-02 3.886e+04 -7.909 2.67e-15 ***
## DuraConf3-2 -1.785e-01 6.761e-02 3.821e+04 -2.640 0.00829 **
## DuraConf4-3 3.698e-01 6.879e-02 3.855e+04 5.376 7.66e-08 ***
## Category2-1:Hemisphere2-1 3.189e-01 2.323e-01 2.856e+01 1.373 0.18044
## Category2-1:DuraConf2-1 5.018e-01 1.251e-01 3.672e+04 4.011 6.05e-05 ***
## Category2-1:DuraConf3-2 3.702e-01 1.343e-01 2.615e+04 2.757 0.00584 **
## Category2-1:DuraConf4-3 1.759e+00 1.371e-01 3.347e+04 12.835 < 2e-16 ***
## Hemisphere2-1:DuraConf2-1 3.713e-03 1.248e-01 3.817e+04 0.030 0.97626
## Hemisphere2-1:DuraConf3-2 -2.309e-01 1.342e-01 3.377e+04 -1.721 0.08521 .
## Hemisphere2-1:DuraConf4-3 -1.775e-01 1.368e-01 3.704e+04 -1.298 0.19430
## Category2-1:Hemisphere2-1:DuraConf2-1 2.960e-01 2.486e-01 3.101e+04 1.191 0.23377
## Category2-1:Hemisphere2-1:DuraConf3-2 -3.908e-02 2.652e-01 1.341e+04 -0.147 0.88287
## Category2-1:Hemisphere2-1:DuraConf4-3 2.456e-01 2.718e-01 2.410e+04 0.904 0.36614
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(lmm.rdc.N170.SC.E2, lmm.zcp.N170.SC.E2, refit = FALSE)4.3.4.4 The extended model
lmm.etd.N170.SC.E2 <- update(lmm.rdc.N170.SC.E2,
formula = MeanAmp ~ Category * Hemisphere * DuraConf +
(1 + Cate_C + Hemi_C + Cate_Hemi | SubjCode) +
(1 | Stimuli))## start par. = 1 1 0 0 0 1 0 0 1 0 1 fn = 222042.5
## At return
## eval: 1134 fn: 221501.14 par: 0.0376527 0.468917 -0.124981 -0.00680051 -0.0181522 0.172586 -0.0130994 -0.0558253 0.351828 -0.213356 0.184805print(summary(lmm.rdc.N170.SC.E2), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Category + Hemisphere + DuraConf + (1 + Cate_C + Hemi_C +
## Cate_Hemi || SubjCode) + (1 | Stimuli) + Category:Hemisphere +
## Category:DuraConf + Hemisphere:DuraConf + Category:Hemisphere:DuraConf
## Data: df.erp.N170.SC.E2
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 221530.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.3076 -0.5935 -0.0001 0.5902 6.7081
##
## Random effects:
## Groups Name Variance Std.Dev.
## Stimuli (Intercept) 0.02205 0.1485
## SubjCode Cate_Hemi 1.29397 1.1375
## SubjCode.1 Hemi_C 1.93011 1.3893
## SubjCode.2 Cate_C 0.70247 0.8381
## SubjCode.3 (Intercept) 3.42443 1.8505
## Residual 15.55810 3.9444
## Number of obs: 39600, groups: Stimuli, 80; SubjCode, 28
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.995e+00 3.508e-01 2.714e+01 -5.687 4.75e-06 ***
## Category2-1 1.217e+00 1.677e-01 2.946e+01 7.253 5.00e-08 ***
## Hemisphere2-1 -1.757e-01 2.662e-01 2.726e+01 -0.660 0.51479
## DuraConf2-1 -4.958e-01 6.270e-02 3.886e+04 -7.909 2.67e-15 ***
## DuraConf3-2 -1.785e-01 6.761e-02 3.821e+04 -2.640 0.00829 **
## DuraConf4-3 3.698e-01 6.879e-02 3.855e+04 5.376 7.66e-08 ***
## Category2-1:Hemisphere2-1 3.189e-01 2.323e-01 2.856e+01 1.373 0.18044
## Category2-1:DuraConf2-1 5.018e-01 1.251e-01 3.672e+04 4.011 6.05e-05 ***
## Category2-1:DuraConf3-2 3.702e-01 1.343e-01 2.615e+04 2.757 0.00584 **
## Category2-1:DuraConf4-3 1.759e+00 1.371e-01 3.347e+04 12.835 < 2e-16 ***
## Hemisphere2-1:DuraConf2-1 3.713e-03 1.248e-01 3.817e+04 0.030 0.97626
## Hemisphere2-1:DuraConf3-2 -2.309e-01 1.342e-01 3.377e+04 -1.721 0.08521 .
## Hemisphere2-1:DuraConf4-3 -1.775e-01 1.368e-01 3.704e+04 -1.298 0.19430
## Category2-1:Hemisphere2-1:DuraConf2-1 2.960e-01 2.486e-01 3.101e+04 1.191 0.23377
## Category2-1:Hemisphere2-1:DuraConf3-2 -3.908e-02 2.652e-01 1.341e+04 -0.147 0.88287
## Category2-1:Hemisphere2-1:DuraConf4-3 2.456e-01 2.718e-01 2.410e+04 0.904 0.36614
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(lmm.rdc.N170.SC.E2, lmm.etd.N170.SC.E2, refit = FALSE)4.3.4.5 The optimal model
lmm.opt.N170.SC.E205 <- lmm.etd.N170.SC.E2
print(summary(lmm.opt.N170.SC.E205), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Category + Hemisphere + DuraConf + (1 + Cate_C + Hemi_C +
## Cate_Hemi | SubjCode) + (1 | Stimuli) + Category:Hemisphere +
## Category:DuraConf + Hemisphere:DuraConf + Category:Hemisphere:DuraConf
## Data: df.erp.N170.SC.E2
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 221501.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.3165 -0.5946 -0.0004 0.5897 6.7418
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## Stimuli (Intercept) 0.02206 0.1485
## SubjCode (Intercept) 3.42096 1.8496
## Cate_C 0.70643 0.8405 -0.59
## Hemi_C 1.92921 1.3890 -0.02 -0.02
## Cate_Hemi 1.29318 1.1372 -0.06 -0.12 -0.73
## Residual 15.55805 3.9444
## Number of obs: 39600, groups: Stimuli, 80; SubjCode, 28
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.996e+00 3.506e-01 2.714e+01 -5.693 4.68e-06 ***
## Category2-1 1.217e+00 1.681e-01 2.945e+01 7.236 5.24e-08 ***
## Hemisphere2-1 -1.775e-01 2.661e-01 2.725e+01 -0.667 0.51031
## DuraConf2-1 -4.936e-01 6.263e-02 3.817e+04 -7.882 3.31e-15 ***
## DuraConf3-2 -1.805e-01 6.747e-02 3.568e+04 -2.674 0.00749 **
## DuraConf4-3 3.715e-01 6.865e-02 3.638e+04 5.411 6.29e-08 ***
## Category2-1:Hemisphere2-1 3.184e-01 2.321e-01 2.853e+01 1.372 0.18085
## Category2-1:DuraConf2-1 5.042e-01 1.248e-01 3.307e+04 4.040 5.36e-05 ***
## Category2-1:DuraConf3-2 3.517e-01 1.335e-01 1.717e+04 2.634 0.00844 **
## Category2-1:DuraConf4-3 1.770e+00 1.365e-01 2.596e+04 12.970 < 2e-16 ***
## Hemisphere2-1:DuraConf2-1 -3.097e-03 1.242e-01 3.044e+04 -0.025 0.98010
## Hemisphere2-1:DuraConf3-2 -2.384e-01 1.329e-01 1.699e+04 -1.794 0.07280 .
## Hemisphere2-1:DuraConf4-3 -1.673e-01 1.354e-01 1.950e+04 -1.235 0.21683
## Category2-1:Hemisphere2-1:DuraConf2-1 2.733e-01 2.465e-01 1.737e+04 1.109 0.26762
## Category2-1:Hemisphere2-1:DuraConf3-2 -5.871e-02 2.597e-01 4.297e+03 -0.226 0.82118
## Category2-1:Hemisphere2-1:DuraConf4-3 2.686e-01 2.675e-01 8.591e+03 1.004 0.31539
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(lmm.opt.N170.SC.E205)4.3.4.6 Diagnostic plots
qqplot_lmer(lmm.opt.N170.SC.E205)
4.3.4.7 Estimated marginal means
emm.N170.SC.E205 <- emmeans(lmm.opt.N170.SC.E205, ~ Category + DuraConf | Hemisphere)
emm.N170.SC.E205## Hemisphere = Left:
## Category DuraConf emmean SE df lower.CL upper.CL
## face 17_guess -1.712560 0.4538844 30.69 -2.638645 -0.7864760
## house 17_guess -1.530219 0.3427950 29.18 -2.231130 -0.8293078
## face 17_low -2.388422 0.4459205 28.59 -3.300995 -1.4758496
## house 17_low -1.838474 0.3420131 28.91 -2.538059 -1.1388892
## face 17_high -2.640200 0.4439514 28.09 -3.549459 -1.7309409
## house 17_high -1.709212 0.3580465 34.68 -2.436328 -0.9820953
## face 200_high -3.003007 0.4469683 28.87 -3.917342 -2.0886717
## house 200_high -0.436159 0.3459560 30.28 -1.142425 0.2701067
##
## Hemisphere = Right:
## Category DuraConf emmean SE df lower.CL upper.CL
## face 17_guess -1.764593 0.4537364 30.67 -2.690398 -0.8387881
## house 17_guess -1.506561 0.3260744 29.31 -2.173154 -0.8399688
## face 17_low -2.580185 0.4458010 28.58 -3.492528 -1.6678427
## house 17_low -1.681280 0.3253093 29.04 -2.346573 -1.0159872
## face 17_high -3.041003 0.4438271 28.08 -3.950024 -2.1319814
## house 17_high -1.819765 0.3415646 35.22 -2.513020 -1.1265102
## face 200_high -3.705354 0.4468824 28.87 -4.619515 -2.7911915
## house 200_high -0.579688 0.3295171 30.58 -1.252118 0.0927434
##
## Degrees-of-freedom method: satterthwaite
## Confidence level used: 0.954.3.4.8 Plots
# emmip(emm.N170.SC.E205, Category ~ DuraConf | Hemisphere, CIs = TRUE) +
# scale_y_continuous(trans = "reverse")duraconf.labels = c("17ms\nguess", "17ms\nlow", "17ms\nhigh", "200ms\nhigh")
n170.conf.LinePlot = {
ggplot(data = data.frame(emm.N170.SC.E205), aes(y = emmean, x = DuraConf, color = Category, group = Category)) + # set the data, varialbes for x and y axises, and the variable for dividing data
geom_point(size = 2) +
geom_line(aes(linetype = Category), size = .85) + # position = "dodge", alpha = .7
scale_linetype_manual(values=c("solid", "dashed")) +
geom_errorbar(mapping = aes(ymin = lower.CL, ymax = upper.CL), linetype = 1, # set the error bar
show.legend = FALSE, width = 0.25, alpha = .5) + # , position = position_dodge(width=0.9)
facet_grid(. ~ Hemisphere, # switch = "x",
labeller = labeller(Hemisphere = hemiLabel)) +
scale_y_reverse(limits = ylimit.n170, breaks = seq(1, -5, -1)) + # set the limit for y axis
scale_x_discrete(labels = duraconf.labels) +
# scale_y_continuous(expand= c(0, 0)) + # remove the space between columns and x axis
labs(x = "Duration and Subjective Confidence", y = expression(paste("Amplitudes (", mu, "V)")), fill = "Category") + # set the names for main, x and y axises title = "",
# scale_x_discrete(labels = c("left", "right")) +
# geom_text(label = c("", "", "", "", "***", "", "", ""), color = "red", size = 6, nudge_y = 0.5, nudge_x = 0.5) + # add starts to the significant columns
theme_bw() +
erp_theme
}
# n170.conf.LinePlot.slide <- {
# n170.conf.LinePlot +
# scale_y_reverse(limits = c(0.8, -4.8), breaks = seq(0, -4, -1)) + # set the limit for y axis
# theme(legend.position="right",
# axis.line = element_line(color="black", size = 3))
# }
# ggsave('N170_confi.png', n170.conf.LinePlot.slide, width = 8.5, height = 4)
n170.conf.LinePlot
4.3.4.9 Contrast
# contra.N170.resp <- contrast(emm.N170.SC.E205, "pairwise", simple = "each", combine = FALSE, adjust = "Bonferroni")
#
# contra.N170.resp
# confint(contra.N170.resp)contra.SC.N170.gen <- contrast(emmeans(lmm.opt.N170.SC.E205, ~ DuraConf | Hemisphere + Category), #
"pairwise", adjust = "Bonferroni")
contra.SC.N170.gen[1:6]## contrast Hemisphere Category estimate SE df t.ratio p.value
## 17_guess - 17_low Left face 0.6758617 0.1395425 38713.17 4.843 <.0001
## 17_guess - 17_high Left face 0.9276398 0.1460077 29514.74 6.353 <.0001
## 17_guess - 200_high Left face 1.2904465 0.1469225 38302.56 8.783 <.0001
## 17_low - 17_high Left face 0.2517781 0.1169049 27206.82 2.154 0.1876
## 17_low - 200_high Left face 0.6145848 0.1203932 38772.86 5.105 <.0001
## 17_high - 200_high Left face 0.3628068 0.1131308 38093.93 3.207 0.0081
##
## Degrees-of-freedom method: satterthwaite
## P value adjustment: bonferroni method for 6 testsconfint(contra.SC.N170.gen[1:6])contra.SC.N170.gen[7:12]## contrast Hemisphere Category estimate SE df t.ratio p.value
## 17_guess - 17_low Right face 0.8155921 0.1393891 37587.05 5.851 <.0001
## 17_guess - 17_high Right face 1.2764096 0.1454448 22943.16 8.776 <.0001
## 17_guess - 200_high Right face 1.9407603 0.1467337 36887.27 13.226 <.0001
## 17_low - 17_high Right face 0.4608175 0.1163862 20095.69 3.959 0.0005
## 17_low - 200_high Right face 1.1251682 0.1202709 37878.28 9.355 <.0001
## 17_high - 200_high Right face 0.6643506 0.1129761 36540.10 5.880 <.0001
##
## Degrees-of-freedom method: satterthwaite
## P value adjustment: bonferroni method for 6 testsconfint(contra.SC.N170.gen[7:12])contra.SC.N170.gen.hemi <- contrast(emmeans(lmm.opt.N170.SC.E205, ~ DuraConf + Hemisphere | Category), #
interaction = "pairwise", adjust = "Bonferroni")
contra.SC.N170.gen.hemi[1:6]## DuraConf_pairwise Hemisphere_pairwise Category estimate SE df t.ratio p.value
## 17_guess - 17_low Left - Right face -0.1397304 0.1969419 37121.28 -0.710 1.0000
## 17_guess - 17_high Left - Right face -0.3487699 0.2052731 21129.08 -1.699 0.5359
## 17_guess - 200_high Left - Right face -0.6503138 0.2073552 36333.86 -3.136 0.0103
## 17_low - 17_high Left - Right face -0.2090395 0.1642874 18263.35 -1.272 1.0000
## 17_low - 200_high Left - Right face -0.5105834 0.1700071 37522.47 -3.003 0.0160
## 17_high - 200_high Left - Right face -0.3015439 0.1596700 35954.30 -1.889 0.3538
##
## Degrees-of-freedom method: satterthwaite
## P value adjustment: bonferroni method for 6 testsconfint(contra.SC.N170.gen.hemi[1:6])contra.SC.N170.spec <- contrast(emmeans(lmm.opt.N170.SC.E205, ~ Category + DuraConf | Hemisphere), #
interaction = "pairwise", adjust = "Bonferroni")
contra.SC.N170.spec[1:6]## Category_pairwise DuraConf_pairwise Hemisphere estimate SE df t.ratio p.value
## face - house 17_guess - 17_low Left 0.3676065 0.1758644 29293.02 2.090 0.2196
## face - house 17_guess - 17_high Left 0.7486470 0.2128595 8673.07 3.517 0.0026
## face - house 17_guess - 200_high Left 2.3845062 0.1868966 34616.75 12.758 <.0001
## face - house 17_low - 17_high Left 0.3810405 0.1874598 11931.61 2.033 0.2527
## face - house 17_low - 200_high Left 2.0168996 0.1659301 38713.15 12.155 <.0001
## face - house 17_high - 200_high Left 1.6358591 0.1919170 19722.87 8.524 <.0001
##
## Degrees-of-freedom method: satterthwaite
## P value adjustment: bonferroni method for 6 testsconfint(contra.SC.N170.spec[1:6])contra.SC.N170.spec[7:12]## Category_pairwise DuraConf_pairwise Hemisphere estimate SE df t.ratio p.value
## face - house 17_guess - 17_low Right 0.6408733 0.1749512 20554.15 3.663 0.0015
## face - house 17_guess - 17_high Right 0.9632057 0.2094177 3906.36 4.599 <.0001
## face - house 17_guess - 200_high Right 2.8676340 0.1862807 29077.10 15.394 <.0001
## face - house 17_low - 17_high Right 0.3223323 0.1850158 5675.07 1.742 0.4892
## face - house 17_low - 200_high Right 2.2267607 0.1657006 37310.22 13.438 <.0001
## face - house 17_high - 200_high Right 1.9044283 0.1902512 11186.98 10.010 <.0001
##
## Degrees-of-freedom method: satterthwaite
## P value adjustment: bonferroni method for 6 testsconfint(contra.SC.N170.spec[7:12])contra.SC.N170.spec.hemi <- contrast(emmeans(lmm.opt.N170.SC.E205, ~ Category + DuraConf + Hemisphere), #
interaction = "pairwise", adjust = "Bonferroni")
contra.SC.N170.spec.hemi## Category_pairwise DuraConf_pairwise Hemisphere_pairwise estimate SE df t.ratio p.value
## face - house 17_guess - 17_low Left - Right -0.2732668 0.2464975 17365.60 -1.109 1.0000
## face - house 17_guess - 17_high Left - Right -0.2145586 0.2931619 2919.40 -0.732 1.0000
## face - house 17_guess - 200_high Left - Right -0.4831278 0.2629621 27102.36 -1.837 0.3971
## face - house 17_low - 17_high Left - Right 0.0587082 0.2597280 4296.86 0.226 1.0000
## face - house 17_low - 200_high Left - Right -0.2098610 0.2341167 36563.69 -0.896 1.0000
## face - house 17_high - 200_high Left - Right -0.2685692 0.2674890 8591.17 -1.004 1.0000
##
## Degrees-of-freedom method: satterthwaite
## P value adjustment: bonferroni method for 6 tests4.4 Linear mixed model with high subjective confidences
tmpBlock.count <- {
clean.beha.205 %>%
filter(Type == "intact") %>%
group_by(SubjCode, Category, Duration, blockID) %>%
summarize(Count_sum = n())
}
avg.resp.long.E205.Block <- {
clean.beha.205 %>%
filter(Type == "intact") %>%
group_by(SubjCode, Category, Duration, blockID, Resp) %>%
summarize(Count = n()) %>%
right_join(tmpBlock.count, by = c("SubjCode", "blockID", "Category", "Duration")) %>%
mutate(RespRate = Count / Count_sum) %>%
ungroup()
}
# descriptive statistics of accuracy for plotting
desc.resp.E205.Block <- {
avg.resp.long.E205.Block %>%
group_by(blockID, Category, Duration, Resp) %>%
summarize(Mean = mean(RespRate)
, N = n()
, SD = if_else(N > 1, sd(RespRate), 0)
, SE = SD/sqrt(N)
, SE.lo = Mean - SE, SE.hi = Mean + SE
, CI = SE * qt(0.975,N)
, Median = median(RespRate)
, Lower = Mean - SD, Upper = Mean + SD # for rainclound plot
) %>%
ungroup() %>%
add_row(blockID = 2, Category = "house", Duration = 200, Resp = 2, Mean = 0, N = 0, SD = 0, SE = 0, SE.lo = 0,
SE.hi = 0, CI = 0, Median = 0, Lower = 0, Upper = 0)
}
avg.resp.wide.E205.Block <- {
avg.resp.long.E205.Block %>%
mutate(Conditions = paste(blockID, Category, Duration, Resp, sep = "_")) %>%
select(SubjCode, Conditions, RespRate) %>%
spread(Conditions, RespRate)
}block.high <- {
avg.resp.long.E205.Block %>%
filter(Category == "face", Duration == 17, Resp == 1, SubjCode != "P513")
}
t.test(filter(block.high, blockID == 1)$RespRate, filter(block.high, blockID == 2)$RespRate, paired = T)##
## Paired t-test
##
## data: filter(block.high, blockID == 1)$RespRate and filter(block.high, blockID == 2)$RespRate
## t = -0.64323, df = 26, p-value = 0.5257
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.10424318 0.05455182
## sample estimates:
## mean of the differences
## -0.02484568knitr::kable(desc.resp.E205.Block, digits = 4)| blockID | Category | Duration | Resp | Mean | N | SD | SE | SE.lo | SE.hi | CI | Median | Lower | Upper |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | face | 17 | 1 | 0.4628 | 28 | 0.3419 | 0.0646 | 0.3982 | 0.5274 | 0.1324 | 0.3812 | 0.1209 | 0.8047 |
| 1 | face | 17 | 2 | 0.3192 | 28 | 0.2240 | 0.0423 | 0.2769 | 0.3615 | 0.0867 | 0.3354 | 0.0952 | 0.5432 |
| 1 | face | 17 | 3 | 0.1737 | 28 | 0.1856 | 0.0351 | 0.1386 | 0.2087 | 0.0719 | 0.0958 | -0.0120 | 0.3593 |
| 1 | face | 17 | 4 | 0.0472 | 25 | 0.0638 | 0.0128 | 0.0344 | 0.0599 | 0.0263 | 0.0208 | -0.0166 | 0.1109 |
| 1 | face | 17 | 5 | 0.0156 | 4 | 0.0202 | 0.0101 | 0.0055 | 0.0257 | 0.0281 | 0.0062 | -0.0046 | 0.0359 |
| 1 | house | 17 | 1 | 0.0120 | 17 | 0.0107 | 0.0026 | 0.0094 | 0.0146 | 0.0055 | 0.0083 | 0.0013 | 0.0227 |
| 1 | house | 17 | 2 | 0.0658 | 28 | 0.0806 | 0.0152 | 0.0505 | 0.0810 | 0.0312 | 0.0417 | -0.0148 | 0.1464 |
| 1 | house | 17 | 3 | 0.4188 | 28 | 0.2770 | 0.0524 | 0.3664 | 0.4711 | 0.1072 | 0.3542 | 0.1417 | 0.6958 |
| 1 | house | 17 | 4 | 0.3890 | 27 | 0.2334 | 0.0449 | 0.3441 | 0.4340 | 0.0922 | 0.4167 | 0.1556 | 0.6225 |
| 1 | house | 17 | 5 | 0.1961 | 19 | 0.2386 | 0.0547 | 0.1413 | 0.2508 | 0.1146 | 0.1042 | -0.0425 | 0.4347 |
| 2 | face | 17 | 1 | 0.5046 | 27 | 0.3326 | 0.0640 | 0.4406 | 0.5686 | 0.1313 | 0.4750 | 0.1721 | 0.8372 |
| 2 | face | 17 | 2 | 0.3786 | 24 | 0.2660 | 0.0543 | 0.3243 | 0.4329 | 0.1121 | 0.3875 | 0.1126 | 0.6446 |
| 2 | face | 17 | 3 | 0.1300 | 25 | 0.1197 | 0.0239 | 0.1061 | 0.1539 | 0.0493 | 0.0750 | 0.0103 | 0.2497 |
| 2 | face | 17 | 4 | 0.0863 | 21 | 0.0828 | 0.0181 | 0.0682 | 0.1044 | 0.0376 | 0.0500 | 0.0035 | 0.1691 |
| 2 | face | 17 | 5 | 0.0225 | 10 | 0.0184 | 0.0058 | 0.0167 | 0.0283 | 0.0130 | 0.0125 | 0.0041 | 0.0409 |
| 2 | face | 200 | 1 | 0.9768 | 28 | 0.0320 | 0.0061 | 0.9707 | 0.9828 | 0.0124 | 0.9875 | 0.9447 | 1.0088 |
| 2 | face | 200 | 2 | 0.0400 | 10 | 0.0403 | 0.0127 | 0.0273 | 0.0527 | 0.0284 | 0.0188 | -0.0003 | 0.0803 |
| 2 | face | 200 | 3 | 0.0208 | 9 | 0.0125 | 0.0042 | 0.0167 | 0.0250 | 0.0094 | 0.0125 | 0.0083 | 0.0333 |
| 2 | face | 200 | 4 | 0.0125 | 1 | 0.0000 | 0.0000 | 0.0125 | 0.0125 | 0.0000 | 0.0125 | 0.0125 | 0.0125 |
| 2 | face | 200 | 5 | 0.0125 | 4 | 0.0000 | 0.0000 | 0.0125 | 0.0125 | 0.0000 | 0.0125 | 0.0125 | 0.0125 |
| 2 | house | 17 | 1 | 0.0156 | 4 | 0.0062 | 0.0031 | 0.0125 | 0.0188 | 0.0087 | 0.0125 | 0.0094 | 0.0219 |
| 2 | house | 17 | 2 | 0.0354 | 18 | 0.0412 | 0.0097 | 0.0257 | 0.0451 | 0.0204 | 0.0250 | -0.0058 | 0.0766 |
| 2 | house | 17 | 3 | 0.3032 | 27 | 0.2482 | 0.0478 | 0.2555 | 0.3510 | 0.0980 | 0.3000 | 0.0550 | 0.5515 |
| 2 | house | 17 | 4 | 0.4696 | 28 | 0.2377 | 0.0449 | 0.4247 | 0.5146 | 0.0920 | 0.4562 | 0.2319 | 0.7074 |
| 2 | house | 17 | 5 | 0.2208 | 27 | 0.2831 | 0.0545 | 0.1664 | 0.2753 | 0.1118 | 0.0750 | -0.0622 | 0.5039 |
| 2 | house | 200 | 1 | 0.0188 | 6 | 0.0105 | 0.0043 | 0.0145 | 0.0230 | 0.0104 | 0.0125 | 0.0083 | 0.0292 |
| 2 | house | 200 | 3 | 0.0150 | 10 | 0.0079 | 0.0025 | 0.0125 | 0.0175 | 0.0056 | 0.0125 | 0.0071 | 0.0229 |
| 2 | house | 200 | 4 | 0.0331 | 20 | 0.0208 | 0.0046 | 0.0285 | 0.0378 | 0.0097 | 0.0250 | 0.0123 | 0.0539 |
| 2 | house | 200 | 5 | 0.9670 | 28 | 0.0262 | 0.0049 | 0.9620 | 0.9719 | 0.0101 | 0.9750 | 0.9408 | 0.9931 |
| 2 | house | 200 | 2 | 0.0000 | 0 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
rt.RainPlot.E205.Block <- {
ggplot(data = avg.resp.long.E205.Block, aes(y = RespRate, x = as.factor(Resp), fill = Category)) +
geom_flat_violin(position = position_nudge(x = .2, y = 0), alpha = .7) +
geom_point(aes(y = RespRate, color = Category), position = position_jitter(width = .15), size = .5, alpha = .8) +
geom_boxplot(aes(y = RespRate), width = .2, outlier.shape = NA, alpha = .7) +
geom_point(data = desc.resp.E205.Block, aes(y = Mean, x = Resp, color = Category), position = position_nudge(x = 0.3), size = 2.5) +
geom_errorbar(data = desc.resp.E205.Block, aes(y = Mean, ymin = Lower, ymax = Upper), position = position_nudge(x = 0.3), width = 0) +
facet_grid(Duration ~ blockID) +
# facet_grid(. ~ Type, switch = "x") + # create two panels to show data
# scale_colour_grey() + # start = .1, end = .6, color for the contour
# scale_fill_grey() + # start = .3, end = .6, color for the fill
# scale_color_brewer(palette = "Set1") + # palette = "RdBu"
# scale_fill_brewer(palette = "Set1") + # palette = "RdBu"
labs(title = "Reaction times for E205", x = "Stimulus Type", y = "Reaction times (ms)", fill = "Duration(ms)", color = "Duration(ms)") + # set the names for main, x and y axises and the legend
# coord_cartesian(ylim = c(0, 1.05)) + # set the limit for y axis
# scale_y_continuous(expand= c(0, 0)) + # remove the space between columns and x axis
theme_bw() + # the backgroud color
plot_theme
}
rt.RainPlot.E205.Block
bloc.labels <- c("1st Half", "2nd Half")
names(bloc.labels) <- c(1, 2)
acc.ColuPlot.E205.Block <- {
ggplot(data = desc.resp.E205.Block, aes(y = Mean, x = Resp, fill = Category)) + # set the data, varialbes for x and y axises, and the variable for dividing data
geom_col(position = "dodge", color = "black", alpha = .7) + # position of columns and countour of columns
facet_grid(Duration ~ blockID,
labeller = labeller(Duration = dura.labels,
blockID = bloc.labels)) +
geom_errorbar(mapping = aes(ymin = SE.lo, ymax = SE.hi), linetype = 1, # set the error bar
show.legend = FALSE, width = 0.25, alpha = .5,
position = position_dodge(width=0.9)) +
geom_hline(yintercept = c(1), linetype = 5, alpha = 0.5) + # add the line for 0.5 and 1 (y)
coord_cartesian(ylim = c(0,1.1)) + # set the limit for y axis
scale_y_continuous(expand= c(0, 0)) + # remove the space between columns and x axis
labs(title = "", x = "Responses", y = "Percentage", fill = "Category") + # set the names for main, x and y axises Subjective Responses for E205
scale_fill_grey() + # set the color for columns
# geom_text(label = c("***", "***", "", ""), color = c("red", "red", "red", "red"), size = 6, nudge_y = 0.05, nudge_x = rep(c(-0.225, 0.225), 2)) + # add starts to the significant columns
erp_theme
}
acc.ColuPlot.E205.Block
df.ratio.E205.Block <- {
clean.beha.205 %>%
filter(Type == "intact") %>%
mutate(isFace = if_else(Category == "face", 1, 0)) %>%
group_by(SubjCode, blockID, Duration, Resp) %>%
summarize(ratio = mean(isFace), Count = n())
}
desc.ratio.E205.Block <- {
df.ratio.E205.Block %>%
group_by(blockID, Duration, Resp) %>%
summarize(Mean = mean(ratio)
, N = n()
, SD = if_else(N > 1, sd(ratio), 0)
, SE = SD/sqrt(N)
, SE.lo = Mean - SE, SE.hi = Mean + SE
, CI = SE * qt(0.975,N)
, Median = median(ratio)
, Lower = Mean - SD, Upper = Mean + SD # for rainclound plot
) %>%
ungroup()
}ratio.ColuPlot.E205.Block <- {
ggplot(data = desc.ratio.E205.Block, aes(y = Mean, x = Resp)) + # set the data, varialbes for x and y axises, and the variable for dividing data
geom_col(position = "dodge", color = "black", alpha = .7) + # position of columns and countour of columns
facet_grid(Duration ~ blockID,
labeller = labeller(Duration = dura.labels,
blockID = bloc.labels)) +
geom_errorbar(mapping = aes(ymin = SE.lo, ymax = SE.hi), linetype = 1, # set the error bar
show.legend = FALSE, width = 0.25, alpha = .5,
position = position_dodge(width=0.9)) +
geom_hline(yintercept = c(0.5, 1), linetype = 5, alpha = 0.5) + # add the line for 0.5 and 1 (y)
coord_cartesian(ylim = c(0,1.1)) + # set the limit for y axis
scale_y_continuous(expand= c(0, 0)) + # remove the space between columns and x axis
labs(title = "", x = "Responses", y = "Percentage") + # set the names for main, x and y axises Subjective Responses for E205
scale_fill_grey() + # set the color for columns
# geom_text(label = c("***", "***", "", ""), color = c("red", "red", "red", "red"), size = 6, nudge_y = 0.05, nudge_x = rep(c(-0.225, 0.225), 2)) + # add starts to the significant columns
plot_theme
}
ratio.ColuPlot.E205.Block
df.erp.high.E2 <- {
df.erp.SC.E2 %>%
filter(Confidence == "high") %>%
mutate(DuraBlock = paste(Duration, Block_ST_table, sep = "_")) %>%
sdif_coding_E205_erp_conf()
}4.4.1 Amplitudes of the P1
# only keep the data for amplitudes of the P1 (correct and incorrect erps)
df.erp.P1.high.E2 <- {
df.erp.high.E2 %>%
filter(Component == "P1")
}
if (saveCSV) {
output.erp.P1.high.E2 <- file.path(folder.nesi, "E205_erp_P1_high.RData")
save(df.erp.P1.high.E2, file = output.erp.P1.high.E2)
}
# the structure of this dataset
head(df.erp.P1.high.E2, 10)4.4.1.1 The maximal model
lmm.max.P1.high.E2 <- lmer(MeanAmp ~ Category * Hemisphere * DuraBlock +
(1 + Cate_C + Hemi_C + Cate_Hemi | SubjCode) +
(1 + Hemi_C | Stimuli),
data = df.erp.P1.high.E2,
verbose = TRUE,
control = lmerControl(optimizer = "bobyqa", # nloptwrap Nelder_Mead
optCtrl = list(maxfun = 1e5)))## start par. = 1 0 1 1 0 0 0 1 0 0 1 0 1 fn = 104824.6
## At return
## eval: 828 fn: 103991.60 par: 0.0607829 0.0132273 0.00000 0.368839 -0.0559292 0.0565168 0.0161722 0.107212 0.0588553 -0.00506718 0.178642 -0.0172574 1.00313e-07## boundary (singular) fit: see ?isSingularprint(summary(lmm.max.P1.high.E2), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Category * Hemisphere * DuraBlock + (1 + Cate_C + Hemi_C + Cate_Hemi | SubjCode) + (1 + Hemi_C | Stimuli)
## Data: df.erp.P1.high.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## REML criterion at convergence: 103991.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.3462 -0.5802 -0.0117 0.5811 6.3206
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## Stimuli (Intercept) 0.059709 0.24436
## Hemi_C 0.002828 0.05318 1.00
## SubjCode (Intercept) 2.198646 1.48278
## Cate_C 0.236322 0.48613 -0.46
## Hemi_C 0.623365 0.78953 0.29 0.13
## Cate_Hemi 0.009455 0.09724 0.67 -0.49 -0.52
## Residual 16.161464 4.02013
## Number of obs: 18456, groups: Stimuli, 80; SubjCode, 28
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.379e+00 2.848e-01 2.794e+01 4.843 4.29e-05 ***
## Category2-1 -3.119e-03 1.354e-01 4.647e+01 -0.023 0.982
## Hemisphere2-1 1.101e-01 1.695e-01 3.103e+01 0.650 0.521
## DuraBlock2-1 -3.110e-02 9.951e-02 1.778e+04 -0.313 0.755
## DuraBlock3-2 1.062e-01 9.877e-02 1.193e+04 1.075 0.282
## Category2-1:Hemisphere2-1 -1.317e-01 1.491e-01 1.563e+03 -0.883 0.377
## Category2-1:DuraBlock2-1 -2.797e-02 1.988e-01 1.724e+04 -0.141 0.888
## Category2-1:DuraBlock3-2 1.071e-01 1.947e-01 5.630e+03 0.550 0.582
## Hemisphere2-1:DuraBlock2-1 4.922e-02 1.975e-01 1.801e+04 0.249 0.803
## Hemisphere2-1:DuraBlock3-2 1.643e-02 1.924e-01 1.163e+04 0.085 0.932
## Category2-1:Hemisphere2-1:DuraBlock2-1 -1.170e-02 3.940e-01 1.818e+04 -0.030 0.976
## Category2-1:Hemisphere2-1:DuraBlock3-2 -1.721e-01 3.737e-01 1.591e+04 -0.461 0.645
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## convergence code: 0
## boundary (singular) fit: see ?isSingular4.4.1.2 The zero-correlation-parameter model
lmm.zcp.P1.high.E2 <- update(lmm.max.P1.high.E2,
formula = MeanAmp ~ Category * Hemisphere * DuraBlock +
(1 + Cate_C + Hemi_C + Cate_Hemi || SubjCode) +
(1 + Hemi_C || Stimuli))## start par. = 1 1 1 1 1 1 fn = 104824.6
## At return
## eval: 398 fn: 103999.59 par: 0.00000 0.0606616 3.15849e-07 0.196283 0.126126 0.371879## boundary (singular) fit: see ?isSingularprint(summary(lmm.zcp.P1.high.E2), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Category + Hemisphere + DuraBlock + (1 + Cate_C + Hemi_C +
## Cate_Hemi || SubjCode) + (1 + Hemi_C || Stimuli) + Category:Hemisphere +
## Category:DuraBlock + Hemisphere:DuraBlock + Category:Hemisphere:DuraBlock
## Data: df.erp.P1.high.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## REML criterion at convergence: 103999.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.3295 -0.5808 -0.0128 0.5814 6.3179
##
## Random effects:
## Groups Name Variance Std.Dev.
## Stimuli Hemi_C 0.00000 0.0000
## Stimuli.1 (Intercept) 0.05947 0.2439
## SubjCode Cate_Hemi 0.00000 0.0000
## SubjCode.1 Hemi_C 0.62267 0.7891
## SubjCode.2 Cate_C 0.25710 0.5071
## SubjCode.3 (Intercept) 2.23511 1.4950
## Residual 16.16202 4.0202
## Number of obs: 18456, groups: Stimuli, 80; SubjCode, 28
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.382e+00 2.872e-01 2.798e+01 4.811 4.66e-05 ***
## Category2-1 6.224e-03 1.392e-01 4.769e+01 0.045 0.965
## Hemisphere2-1 1.018e-01 1.696e-01 3.174e+01 0.601 0.552
## DuraBlock2-1 -3.233e-02 9.964e-02 1.821e+04 -0.324 0.746
## DuraBlock3-2 1.035e-01 9.925e-02 1.579e+04 1.043 0.297
## Category2-1:Hemisphere2-1 -1.448e-01 1.482e-01 1.785e+04 -0.977 0.328
## Category2-1:DuraBlock2-1 -2.736e-02 1.991e-01 1.794e+04 -0.137 0.891
## Category2-1:DuraBlock3-2 9.423e-02 1.962e-01 9.184e+03 0.480 0.631
## Hemisphere2-1:DuraBlock2-1 4.850e-02 1.976e-01 1.831e+04 0.245 0.806
## Hemisphere2-1:DuraBlock3-2 2.796e-02 1.929e-01 1.636e+04 0.145 0.885
## Category2-1:Hemisphere2-1:DuraBlock2-1 -6.056e-03 3.941e-01 1.832e+04 -0.015 0.988
## Category2-1:Hemisphere2-1:DuraBlock3-2 -1.563e-01 3.742e-01 1.830e+04 -0.418 0.676
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## convergence code: 0
## boundary (singular) fit: see ?isSingular4.4.1.3 The reduced model
summary(rePCA(lmm.zcp.P1.high.E2))## $Stimuli
## Importance of components:
## [,1] [,2]
## Standard deviation 0.06066 0
## Proportion of Variance 1.00000 0
## Cumulative Proportion 1.00000 1
##
## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4]
## Standard deviation 0.3719 0.1963 0.12613 0
## Proportion of Variance 0.7176 0.1999 0.08254 0
## Cumulative Proportion 0.7176 0.9175 1.00000 1lmm.rdc.P1.high.E2 <- update(lmm.zcp.P1.high.E2,
formula = MeanAmp ~ Category * Hemisphere * DuraBlock +
(1 + Cate_C + Hemi_C || SubjCode) +
(1 | Stimuli))## start par. = 1 1 1 1 fn = 104459.8
## At return
## eval: 149 fn: 103999.59 par: 0.0606616 0.196283 0.126126 0.371879print(summary(lmm.rdc.P1.high.E2), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Category + Hemisphere + DuraBlock + (1 + Cate_C + Hemi_C ||
## SubjCode) + (1 | Stimuli) + Category:Hemisphere + Category:DuraBlock +
## Hemisphere:DuraBlock + Category:Hemisphere:DuraBlock
## Data: df.erp.P1.high.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## REML criterion at convergence: 103999.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.3295 -0.5808 -0.0128 0.5814 6.3179
##
## Random effects:
## Groups Name Variance Std.Dev.
## Stimuli (Intercept) 0.05947 0.2439
## SubjCode Hemi_C 0.62267 0.7891
## SubjCode.1 Cate_C 0.25710 0.5070
## SubjCode.2 (Intercept) 2.23511 1.4950
## Residual 16.16202 4.0202
## Number of obs: 18456, groups: Stimuli, 80; SubjCode, 28
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.382e+00 2.872e-01 2.798e+01 4.811 4.66e-05 ***
## Category2-1 6.224e-03 1.392e-01 4.769e+01 0.045 0.965
## Hemisphere2-1 1.018e-01 1.696e-01 3.174e+01 0.601 0.552
## DuraBlock2-1 -3.233e-02 9.964e-02 1.821e+04 -0.324 0.746
## DuraBlock3-2 1.035e-01 9.925e-02 1.579e+04 1.043 0.297
## Category2-1:Hemisphere2-1 -1.448e-01 1.482e-01 1.785e+04 -0.977 0.328
## Category2-1:DuraBlock2-1 -2.736e-02 1.991e-01 1.794e+04 -0.137 0.891
## Category2-1:DuraBlock3-2 9.423e-02 1.962e-01 9.184e+03 0.480 0.631
## Hemisphere2-1:DuraBlock2-1 4.850e-02 1.976e-01 1.831e+04 0.245 0.806
## Hemisphere2-1:DuraBlock3-2 2.796e-02 1.929e-01 1.636e+04 0.145 0.885
## Category2-1:Hemisphere2-1:DuraBlock2-1 -6.056e-03 3.941e-01 1.832e+04 -0.015 0.988
## Category2-1:Hemisphere2-1:DuraBlock3-2 -1.563e-01 3.742e-01 1.830e+04 -0.418 0.676
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(lmm.rdc.P1.high.E2, lmm.zcp.P1.high.E2, refit = FALSE)4.4.1.4 The extended model
lmm.etd.P1.high.E2 <- update(lmm.rdc.P1.high.E2,
formula = MeanAmp ~ Category * Hemisphere * DuraBlock +
(1 + Cate_C + Hemi_C | SubjCode) +
(1 | Stimuli))## start par. = 1 1 0 0 1 0 1 fn = 104459.8
## At return
## eval: 348 fn: 103992.90 par: 0.0606161 0.368821 -0.0560006 0.0540839 0.107121 0.0594284 0.180417print(summary(lmm.etd.P1.high.E2), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Category + Hemisphere + DuraBlock + (1 + Cate_C + Hemi_C |
## SubjCode) + (1 | Stimuli) + Category:Hemisphere + Category:DuraBlock +
## Hemisphere:DuraBlock + Category:Hemisphere:DuraBlock
## Data: df.erp.P1.high.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## REML criterion at convergence: 103992.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.3330 -0.5802 -0.0119 0.5800 6.3237
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## Stimuli (Intercept) 0.05939 0.2437
## SubjCode (Intercept) 2.19869 1.4828
## Cate_C 0.23616 0.4860 -0.46
## Hemi_C 0.63049 0.7940 0.27 0.14
## Residual 16.16335 4.0204
## Number of obs: 18456, groups: Stimuli, 80; SubjCode, 28
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.379e+00 2.848e-01 2.794e+01 4.842 4.29e-05 ***
## Category2-1 -3.143e-03 1.353e-01 4.641e+01 -0.023 0.982
## Hemisphere2-1 1.061e-01 1.703e-01 3.162e+01 0.623 0.538
## DuraBlock2-1 -3.146e-02 9.952e-02 1.778e+04 -0.316 0.752
## DuraBlock3-2 1.067e-01 9.878e-02 1.192e+04 1.081 0.280
## Category2-1:Hemisphere2-1 -1.444e-01 1.481e-01 1.772e+04 -0.975 0.330
## Category2-1:DuraBlock2-1 -2.871e-02 1.988e-01 1.724e+04 -0.144 0.885
## Category2-1:DuraBlock3-2 1.074e-01 1.947e-01 5.619e+03 0.552 0.581
## Hemisphere2-1:DuraBlock2-1 4.903e-02 1.976e-01 1.830e+04 0.248 0.804
## Hemisphere2-1:DuraBlock3-2 2.236e-02 1.928e-01 1.603e+04 0.116 0.908
## Category2-1:Hemisphere2-1:DuraBlock2-1 -9.847e-03 3.941e-01 1.832e+04 -0.025 0.980
## Category2-1:Hemisphere2-1:DuraBlock3-2 -1.557e-01 3.742e-01 1.829e+04 -0.416 0.677
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(lmm.etd.P1.high.E2, lmm.rdc.P1.high.E2, refit = FALSE)4.4.1.5 The optimal model
lmm.opt.P1.high.E205 <- lmm.etd.P1.high.E2
summary(lmm.opt.P1.high.E205)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Category + Hemisphere + DuraBlock + (1 + Cate_C + Hemi_C |
## SubjCode) + (1 | Stimuli) + Category:Hemisphere + Category:DuraBlock +
## Hemisphere:DuraBlock + Category:Hemisphere:DuraBlock
## Data: df.erp.P1.high.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## REML criterion at convergence: 103992.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.3330 -0.5802 -0.0119 0.5800 6.3237
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## Stimuli (Intercept) 0.05939 0.2437
## SubjCode (Intercept) 2.19869 1.4828
## Cate_C 0.23616 0.4860 -0.46
## Hemi_C 0.63049 0.7940 0.27 0.14
## Residual 16.16335 4.0204
## Number of obs: 18456, groups: Stimuli, 80; SubjCode, 28
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.379e+00 2.848e-01 2.794e+01 4.842 4.29e-05 ***
## Category2-1 -3.143e-03 1.353e-01 4.641e+01 -0.023 0.982
## Hemisphere2-1 1.061e-01 1.703e-01 3.162e+01 0.623 0.538
## DuraBlock2-1 -3.146e-02 9.952e-02 1.778e+04 -0.316 0.752
## DuraBlock3-2 1.067e-01 9.878e-02 1.192e+04 1.081 0.280
## Category2-1:Hemisphere2-1 -1.444e-01 1.481e-01 1.772e+04 -0.975 0.330
## Category2-1:DuraBlock2-1 -2.871e-02 1.988e-01 1.724e+04 -0.144 0.885
## Category2-1:DuraBlock3-2 1.074e-01 1.947e-01 5.619e+03 0.552 0.581
## Hemisphere2-1:DuraBlock2-1 4.903e-02 1.976e-01 1.830e+04 0.248 0.804
## Hemisphere2-1:DuraBlock3-2 2.236e-02 1.928e-01 1.603e+04 0.116 0.908
## Category2-1:Hemisphere2-1:DuraBlock2-1 -9.847e-03 3.941e-01 1.832e+04 -0.025 0.980
## Category2-1:Hemisphere2-1:DuraBlock3-2 -1.557e-01 3.742e-01 1.829e+04 -0.416 0.677
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ctg2-1 Hms2-1 DrB2-1 DrB3-2 Ct2-1:H2-1 C2-1:DB2 C2-1:DB3 H2-1:DB2 H2-1:DB3 C2-1:H2-1:DB2
## Category2-1 -0.267
## Hemisphr2-1 0.238 0.089
## DuraBlck2-1 0.036 0.027 -0.001
## DuraBlck3-2 -0.082 -0.193 -0.004 -0.660
## Ctg2-1:H2-1 0.001 0.005 0.184 -0.001 -0.004
## Ct2-1:DB2-1 0.007 0.158 -0.001 0.442 -0.252 -0.001
## Ct2-1:DB3-2 -0.047 -0.321 -0.005 -0.254 0.333 -0.004 -0.674
## Hm2-1:DB2-1 0.000 -0.001 0.131 0.001 0.001 0.070 0.001 0.001
## Hm2-1:DB3-2 -0.001 -0.007 -0.242 0.001 0.004 -0.279 0.001 0.007 -0.685
## C2-1:H2-1:DB2 0.000 0.000 0.033 0.001 0.000 0.316 0.000 0.000 0.439 -0.268
## C2-1:H2-1:DB3 -0.001 -0.002 -0.125 0.000 0.002 -0.468 0.000 0.002 -0.273 0.309 -0.714anova(lmm.opt.P1.high.E205)4.4.1.6 Diagnostic plots
qqplot_lmer(lmm.opt.P1.high.E205)
4.4.1.7 Estimated marginal means
emm.P1.high.E205 <- emmeans(lmm.opt.P1.high.E205, ~ DuraBlock | Category + Hemisphere)
emm.P1.high.E205## Category = face, Hemisphere = Left:
## DuraBlock emmean SE df lower.CL upper.CL
## 17_1 1.320018 0.3099943 30.62 0.6874649 1.952572
## 17_2 1.275938 0.3261743 37.54 0.6153664 1.936510
## 200_2 1.278862 0.3116806 31.33 0.6434601 1.914265
##
## Category = house, Hemisphere = Left:
## DuraBlock emmean SE df lower.CL upper.CL
## 17_1 1.343170 0.2970638 47.27 0.7456435 1.940697
## 17_2 1.275302 0.3226546 66.22 0.6311419 1.919462
## 200_2 1.463480 0.2674223 31.68 0.9185459 2.008415
##
## Category = face, Hemisphere = Right:
## DuraBlock emmean SE df lower.CL upper.CL
## 17_1 1.428909 0.3422225 29.75 0.7297558 2.128062
## 17_2 1.438786 0.3569574 35.22 0.7142875 2.163284
## 200_2 1.541903 0.3438713 30.36 0.8399701 2.243835
##
## Category = house, Hemisphere = Right:
## DuraBlock emmean SE df lower.CL upper.CL
## 17_1 1.366141 0.3376954 41.64 0.6844687 2.047814
## 17_2 1.342383 0.3600515 53.99 0.6205191 2.064247
## 200_2 1.475083 0.3105991 30.11 0.8408562 2.109311
##
## Degrees-of-freedom method: satterthwaite
## Confidence level used: 0.954.4.1.8 Plots
# emmip(emm.P1.high.E205, Category ~ DuraBlock | Hemisphere, CIs = TRUE)durablock.labels = c("17ms\nhalf 1", "17ms\nhalf 2", "200ms\nhalf 2")
p1.high.LinePlot = {
ggplot(data = data.frame(emm.P1.high.E205), aes(y = emmean, x = DuraBlock, color = Category, group = Category)) + # set the data, varialbes for x and y axises, and the variable for dividing data
geom_point(size = 2) +
geom_line(aes(linetype = Category), size = .85) + # position = "dodge", alpha = .7
scale_linetype_manual(values=c("solid", "dashed")) +
geom_errorbar(mapping = aes(ymin = lower.CL, ymax = upper.CL), linetype = 1, # set the error bar
show.legend = FALSE, width = 0.25, alpha = .5) + # , position = position_dodge(width=0.9)
facet_grid(. ~ Hemisphere, # switch = "x",
labeller = labeller(Hemisphere = hemiLabel)) +
scale_y_reverse(limits = ylimit.p1) + # set the limit for y axis
scale_x_discrete(labels = durablock.labels) +
# scale_y_continuous(expand= c(0, 0)) + # remove the space between columns and x axis
labs(x = "Duration and Subjective Confidence", y = expression(paste("Amplitudes (", mu, "V)")), fill = "Category") + # set the names for main, x and y axises title = "",
# scale_x_discrete(labels = c("left", "right")) +
# geom_text(label = c("", "", "", "", "***", "", "", ""), color = "red", size = 6, nudge_y = 0.5, nudge_x = 0.5) + # add starts to the significant columns
theme_bw() +
erp_theme
}
# p1.high.LinePlot.slide <- {
# p1.high.LinePlot +
# theme(legend.position="right",
# axis.line = element_line(color="black", size = 3))
# }
# ggsave('P1_high.png', p1.high.LinePlot.slide, width = 7.5, height = 4)
p1.high.LinePlot
4.4.1.9 Contrast
# contrast(emmeans(lmm.opt.P1.high.E205, ~ DuraBlock | Category + Hemisphere), "pairwise", simple = "each", combine = TRUE)contra.high.P1.gen <- contrast(emmeans(lmm.opt.P1.high.E205, ~ DuraBlock | Hemisphere + Category), #
"pairwise", adjust = "none")
contra.high.P1.gen[1:3]## contrast Hemisphere Category estimate SE df t.ratio p.value
## 17_1 - 17_2 Left face 0.04407999 0.1482603 18312.03 0.297 0.7662
## 17_1 - 200_2 Left face 0.04115600 0.1227113 15818.16 0.335 0.7373
## 17_2 - 200_2 Left face -0.00292399 0.1591442 17711.71 -0.018 0.9853
##
## Degrees-of-freedom method: satterthwaiteconfint(contra.high.P1.gen[1:3])contra.high.P1.gen[4:6]## contrast Hemisphere Category estimate SE df t.ratio p.value
## 17_1 - 17_2 Right face -0.0098768 0.1481876 18277.67 -0.067 0.9469
## 17_1 - 200_2 Right face -0.1129938 0.1223560 14516.06 -0.923 0.3558
## 17_2 - 200_2 Right face -0.1031170 0.1589035 17271.69 -0.649 0.5164
##
## Degrees-of-freedom method: satterthwaiteconfint(contra.high.P1.gen[4:6])contra.high.P1.gen.hemi <- contrast(emmeans(lmm.opt.P1.high.E205, ~ DuraBlock + Hemisphere | Category), #
interaction = "pairwise", adjust = "none")
contra.high.P1.gen.hemi## Category = face:
## DuraBlock_pairwise Hemisphere_pairwise estimate SE df t.ratio p.value
## 17_1 - 17_2 Left - Right 0.05395679 0.2091158 18274.58 0.258 0.7964
## 17_1 - 200_2 Left - Right 0.15414976 0.1710029 15420.21 0.901 0.3674
## 17_2 - 200_2 Left - Right 0.10019297 0.2233042 17648.80 0.449 0.6537
##
## Category = house:
## DuraBlock_pairwise Hemisphere_pairwise estimate SE df t.ratio p.value
## 17_1 - 17_2 Left - Right 0.04410988 0.3347093 18314.99 0.132 0.8952
## 17_1 - 200_2 Left - Right -0.01136829 0.2472187 14349.79 -0.046 0.9633
## 17_2 - 200_2 Left - Right -0.05547817 0.3073727 17460.49 -0.180 0.8568
##
## Degrees-of-freedom method: satterthwaitecontra.high.P1.spec <- contrast(emmeans(lmm.opt.P1.high.E205, ~ Category + DuraBlock | Hemisphere), #
interaction = "pairwise", adjust = "Bonferroni")
contra.high.P1.spec## Hemisphere = Left:
## Category_pairwise DuraBlock_pairwise estimate SE df t.ratio p.value
## face - house 17_1 - 17_2 -0.02378829 0.2798066 18069.83 -0.085 1.0000
## face - house 17_1 - 200_2 0.16146633 0.2148371 3745.19 0.752 1.0000
## face - house 17_2 - 200_2 0.18525462 0.2697033 10769.68 0.687 1.0000
##
## Hemisphere = Right:
## Category_pairwise DuraBlock_pairwise estimate SE df t.ratio p.value
## face - house 17_1 - 17_2 -0.03363520 0.2799462 18184.19 -0.120 1.0000
## face - house 17_1 - 200_2 -0.00405172 0.2160355 4863.00 -0.019 1.0000
## face - house 17_2 - 200_2 0.02958348 0.2703208 12065.72 0.109 1.0000
##
## Degrees-of-freedom method: satterthwaite
## P value adjustment: bonferroni method for 3 testsconfint(contra.high.P1.spec)contra.high.P1.spec.hemi <- contrast(emmeans(lmm.opt.P1.high.E205, ~ Category + DuraBlock + Hemisphere), #
interaction = "pairwise", adjust = "Bonferroni")
contra.high.P1.spec.hemi## Category_pairwise DuraBlock_pairwise Hemisphere_pairwise estimate SE df t.ratio p.value
## face - house 17_1 - 17_2 Left - Right 0.00984691 0.3940770 18315.17 0.025 1.0000
## face - house 17_1 - 200_2 Left - Right 0.16551805 0.2910815 18026.44 0.569 1.0000
## face - house 17_2 - 200_2 Left - Right 0.15567114 0.3741917 18291.34 0.416 1.0000
##
## Degrees-of-freedom method: satterthwaite
## P value adjustment: bonferroni method for 3 tests4.4.2 Amplitudes of the N170
# only keep the data for amplitudes of the N170 (correct and incorrect erps)
df.erp.N170.high.E2 <- {
df.erp.high.E2 %>%
filter(Component == "N170")
}
if (saveCSV) {
output.erp.N170.high.E2 <- file.path(folder.nesi, "E205_erp_N170_high.RData")
save(df.erp.N170.high.E2, file = output.erp.N170.high.E2)
}
# the structure of this dataset
head(df.erp.N170.high.E2, 10)4.4.2.1 The maximal model
lmm.max.N170.high.E2 <- lmer(MeanAmp ~ Category * Hemisphere * DuraBlock +
(1 + Cate_C + Hemi_C + Cate_Hemi | SubjCode) +
(1 + Hemi_C | Stimuli),
data = df.erp.N170.high.E2,
verbose = TRUE,
control = lmerControl(optimizer = "bobyqa", # nloptwrap Nelder_Mead
optCtrl = list(maxfun = 1e5)))## start par. = 1 0 1 1 0 0 0 1 0 0 1 0 1 fn = 105474.1
## At return
## eval: 1055 fn: 104763.30 par: 0.0573010 0.0277337 0.00000 0.454977 -0.134782 -0.0159484 -0.0974501 0.235464 -0.0364190 -0.107638 0.347869 -0.233303 0.243925## boundary (singular) fit: see ?isSingularprint(summary(lmm.max.N170.high.E2), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Category * Hemisphere * DuraBlock + (1 + Cate_C + Hemi_C + Cate_Hemi | SubjCode) + (1 + Hemi_C | Stimuli)
## Data: df.erp.N170.high.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## REML criterion at convergence: 104763.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.6334 -0.5935 -0.0012 0.5937 6.1298
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## Stimuli (Intercept) 0.05502 0.2346
## Hemi_C 0.01289 0.1135 1.00
## SubjCode (Intercept) 3.46891 1.8625
## Cate_C 1.23353 1.1106 -0.50
## Hemi_C 2.05438 1.4333 -0.05 -0.07
## Cate_Hemi 2.26249 1.5042 -0.27 -0.12 -0.59
## Residual 16.75767 4.0936
## Number of obs: 18456, groups: Stimuli, 80; SubjCode, 28
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.319e+00 3.558e-01 2.775e+01 -6.517 4.80e-07 ***
## Category2-1 1.625e+00 2.339e-01 3.373e+01 6.949 5.42e-08 ***
## Hemisphere2-1 -3.835e-01 2.846e-01 2.969e+01 -1.348 0.188
## DuraBlock2-1 -8.716e-01 1.016e-01 1.831e+04 -8.581 < 2e-16 ***
## DuraBlock3-2 1.019e+00 1.014e-01 1.710e+04 10.052 < 2e-16 ***
## Category2-1:Hemisphere2-1 4.113e-01 3.317e-01 3.519e+01 1.240 0.223
## Category2-1:DuraBlock2-1 -1.442e-01 2.030e-01 1.827e+04 -0.710 0.478
## Category2-1:DuraBlock3-2 1.909e+00 2.018e-01 1.457e+04 9.460 < 2e-16 ***
## Hemisphere2-1:DuraBlock2-1 -1.955e-01 2.024e-01 1.765e+04 -0.966 0.334
## Hemisphere2-1:DuraBlock3-2 4.096e-02 1.997e-01 1.025e+04 0.205 0.837
## Category2-1:Hemisphere2-1:DuraBlock2-1 4.030e-01 4.045e-01 1.739e+04 0.996 0.319
## Category2-1:Hemisphere2-1:DuraBlock3-2 3.517e-03 3.962e-01 6.953e+03 0.009 0.993
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## convergence code: 0
## boundary (singular) fit: see ?isSingular4.4.2.2 The zero-correlation-parameter model
lmm.zcp.N170.high.E2 <- update(lmm.max.N170.high.E2,
formula = MeanAmp ~ Category * Hemisphere * DuraBlock +
(1 + Cate_C + Hemi_C + Cate_Hemi || SubjCode) +
(1 + Hemi_C || Stimuli))## start par. = 1 1 1 1 1 1 fn = 105474.1
## At return
## eval: 483 fn: 104787.06 par: 1.26220e-07 0.0562734 0.374364 0.356540 0.272915 0.456967## boundary (singular) fit: see ?isSingularprint(summary(lmm.zcp.N170.high.E2), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Category + Hemisphere + DuraBlock + (1 + Cate_C + Hemi_C +
## Cate_Hemi || SubjCode) + (1 + Hemi_C || Stimuli) + Category:Hemisphere +
## Category:DuraBlock + Hemisphere:DuraBlock + Category:Hemisphere:DuraBlock
## Data: df.erp.N170.high.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## REML criterion at convergence: 104787.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.6562 -0.5916 0.0000 0.5928 6.1351
##
## Random effects:
## Groups Name Variance Std.Dev.
## Stimuli Hemi_C 0.00000 0.0000
## Stimuli.1 (Intercept) 0.05308 0.2304
## SubjCode Cate_Hemi 2.34918 1.5327
## SubjCode.1 Hemi_C 2.13081 1.4597
## SubjCode.2 Cate_C 1.24849 1.1174
## SubjCode.3 (Intercept) 3.50023 1.8709
## Residual 16.76210 4.0942
## Number of obs: 18456, groups: Stimuli, 80; SubjCode, 28
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.316e+00 3.574e-01 2.775e+01 -6.480 5.30e-07 ***
## Category2-1 1.639e+00 2.352e-01 3.368e+01 6.968 5.16e-08 ***
## Hemisphere2-1 -3.760e-01 2.898e-01 2.981e+01 -1.297 0.205
## DuraBlock2-1 -8.721e-01 1.017e-01 1.836e+04 -8.579 < 2e-16 ***
## DuraBlock3-2 1.015e+00 1.017e-01 1.820e+04 9.981 < 2e-16 ***
## Category2-1:Hemisphere2-1 4.367e-01 3.389e-01 3.544e+01 1.289 0.206
## Category2-1:DuraBlock2-1 -1.426e-01 2.032e-01 1.835e+04 -0.702 0.483
## Category2-1:DuraBlock3-2 1.888e+00 2.028e-01 1.705e+04 9.314 < 2e-16 ***
## Hemisphere2-1:DuraBlock2-1 -1.984e-01 2.029e-01 1.829e+04 -0.978 0.328
## Hemisphere2-1:DuraBlock3-2 3.172e-02 2.019e-01 1.734e+04 0.157 0.875
## Category2-1:Hemisphere2-1:DuraBlock2-1 4.145e-01 4.057e-01 1.823e+04 1.022 0.307
## Category2-1:Hemisphere2-1:DuraBlock3-2 -4.034e-02 4.019e-01 1.438e+04 -0.100 0.920
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## convergence code: 0
## boundary (singular) fit: see ?isSingular4.4.2.3 The reduced model
summary(rePCA(lmm.zcp.N170.high.E2))## $Stimuli
## Importance of components:
## [,1] [,2]
## Standard deviation 0.05627 0
## Proportion of Variance 1.00000 0
## Cumulative Proportion 1.00000 1
##
## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4]
## Standard deviation 0.4570 0.3744 0.3565 0.2729
## Proportion of Variance 0.3793 0.2545 0.2309 0.1353
## Cumulative Proportion 0.3793 0.6338 0.8647 1.0000lmm.rdc.N170.high.E2 <- update(lmm.zcp.N170.high.E2,
formula = MeanAmp ~ Category * Hemisphere * DuraBlock +
(1 + Cate_C + Hemi_C + Cate_Hemi || SubjCode) +
(1 | Stimuli))## start par. = 1 1 1 1 1 fn = 105207.8
## At return
## eval: 259 fn: 104787.06 par: 0.0562734 0.374363 0.356541 0.272917 0.456967print(summary(lmm.rdc.N170.high.E2), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Category + Hemisphere + DuraBlock + (1 + Cate_C + Hemi_C +
## Cate_Hemi || SubjCode) + (1 | Stimuli) + Category:Hemisphere +
## Category:DuraBlock + Hemisphere:DuraBlock + Category:Hemisphere:DuraBlock
## Data: df.erp.N170.high.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## REML criterion at convergence: 104787.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.6562 -0.5916 0.0000 0.5928 6.1351
##
## Random effects:
## Groups Name Variance Std.Dev.
## Stimuli (Intercept) 0.05308 0.2304
## SubjCode Cate_Hemi 2.34917 1.5327
## SubjCode.1 Hemi_C 2.13082 1.4597
## SubjCode.2 Cate_C 1.24850 1.1174
## SubjCode.3 (Intercept) 3.50024 1.8709
## Residual 16.76210 4.0942
## Number of obs: 18456, groups: Stimuli, 80; SubjCode, 28
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.316e+00 3.574e-01 2.775e+01 -6.480 5.30e-07 ***
## Category2-1 1.639e+00 2.352e-01 3.368e+01 6.968 5.16e-08 ***
## Hemisphere2-1 -3.760e-01 2.898e-01 2.981e+01 -1.297 0.205
## DuraBlock2-1 -8.721e-01 1.017e-01 1.836e+04 -8.579 < 2e-16 ***
## DuraBlock3-2 1.015e+00 1.017e-01 1.820e+04 9.981 < 2e-16 ***
## Category2-1:Hemisphere2-1 4.367e-01 3.389e-01 3.544e+01 1.289 0.206
## Category2-1:DuraBlock2-1 -1.426e-01 2.032e-01 1.835e+04 -0.702 0.483
## Category2-1:DuraBlock3-2 1.888e+00 2.028e-01 1.705e+04 9.314 < 2e-16 ***
## Hemisphere2-1:DuraBlock2-1 -1.984e-01 2.029e-01 1.829e+04 -0.978 0.328
## Hemisphere2-1:DuraBlock3-2 3.172e-02 2.019e-01 1.734e+04 0.157 0.875
## Category2-1:Hemisphere2-1:DuraBlock2-1 4.145e-01 4.057e-01 1.823e+04 1.022 0.307
## Category2-1:Hemisphere2-1:DuraBlock3-2 -4.034e-02 4.019e-01 1.438e+04 -0.100 0.920
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(lmm.rdc.N170.high.E2, lmm.zcp.N170.high.E2, refit = FALSE)4.4.2.4 The extended model
lmm.etd.N170.high.E2 <- update(lmm.rdc.N170.high.E2,
formula = MeanAmp ~ Category * Hemisphere * DuraBlock +
(1 + Cate_C + Hemi_C + Cate_Hemi | SubjCode) +
(1 | Stimuli))## start par. = 1 1 0 0 0 1 0 0 1 0 1 fn = 105207.8
## At return
## eval: 903 fn: 104765.64 par: 0.0562480 0.454957 -0.134602 -0.0160593 -0.0976551 0.235449 -0.0361790 -0.107527 0.347685 -0.233521 0.243953print(summary(lmm.etd.N170.high.E2), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Category + Hemisphere + DuraBlock + (1 + Cate_C + Hemi_C +
## Cate_Hemi | SubjCode) + (1 | Stimuli) + Category:Hemisphere +
## Category:DuraBlock + Hemisphere:DuraBlock + Category:Hemisphere:DuraBlock
## Data: df.erp.N170.high.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## REML criterion at convergence: 104765.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.6367 -0.5923 -0.0010 0.5935 6.1423
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## Stimuli (Intercept) 0.05303 0.2303
## SubjCode (Intercept) 3.46964 1.8627
## Cate_C 1.23296 1.1104 -0.50
## Hemi_C 2.05262 1.4327 -0.05 -0.07
## Cate_Hemi 2.26537 1.5051 -0.27 -0.12 -0.59
## Residual 16.76265 4.0942
## Number of obs: 18456, groups: Stimuli, 80; SubjCode, 28
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.319e+00 3.558e-01 2.774e+01 -6.517 4.82e-07 ***
## Category2-1 1.626e+00 2.336e-01 3.361e+01 6.960 5.34e-08 ***
## Hemisphere2-1 -3.885e-01 2.842e-01 2.957e+01 -1.367 0.182
## DuraBlock2-1 -8.726e-01 1.016e-01 1.832e+04 -8.590 < 2e-16 ***
## DuraBlock3-2 1.019e+00 1.014e-01 1.712e+04 10.054 < 2e-16 ***
## Category2-1:Hemisphere2-1 4.041e-01 3.309e-01 3.480e+01 1.221 0.230
## Category2-1:DuraBlock2-1 -1.453e-01 2.031e-01 1.828e+04 -0.716 0.474
## Category2-1:DuraBlock3-2 1.908e+00 2.018e-01 1.459e+04 9.454 < 2e-16 ***
## Hemisphere2-1:DuraBlock2-1 -1.971e-01 2.024e-01 1.765e+04 -0.974 0.330
## Hemisphere2-1:DuraBlock3-2 5.056e-02 1.997e-01 1.025e+04 0.253 0.800
## Category2-1:Hemisphere2-1:DuraBlock2-1 3.992e-01 4.045e-01 1.739e+04 0.987 0.324
## Category2-1:Hemisphere2-1:DuraBlock3-2 1.533e-02 3.962e-01 6.960e+03 0.039 0.969
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(lmm.etd.N170.high.E2, lmm.rdc.N170.high.E2, refit = FALSE)4.4.2.5 The optimal model
lmm.opt.N170.high.E205 <- lmm.etd.N170.high.E2
summary(lmm.opt.N170.high.E205)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Category + Hemisphere + DuraBlock + (1 + Cate_C + Hemi_C +
## Cate_Hemi | SubjCode) + (1 | Stimuli) + Category:Hemisphere +
## Category:DuraBlock + Hemisphere:DuraBlock + Category:Hemisphere:DuraBlock
## Data: df.erp.N170.high.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## REML criterion at convergence: 104765.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.6367 -0.5923 -0.0010 0.5935 6.1423
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## Stimuli (Intercept) 0.05303 0.2303
## SubjCode (Intercept) 3.46964 1.8627
## Cate_C 1.23296 1.1104 -0.50
## Hemi_C 2.05262 1.4327 -0.05 -0.07
## Cate_Hemi 2.26537 1.5051 -0.27 -0.12 -0.59
## Residual 16.76265 4.0942
## Number of obs: 18456, groups: Stimuli, 80; SubjCode, 28
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.319e+00 3.558e-01 2.774e+01 -6.517 4.82e-07 ***
## Category2-1 1.626e+00 2.336e-01 3.361e+01 6.960 5.34e-08 ***
## Hemisphere2-1 -3.885e-01 2.842e-01 2.957e+01 -1.367 0.182
## DuraBlock2-1 -8.726e-01 1.016e-01 1.832e+04 -8.590 < 2e-16 ***
## DuraBlock3-2 1.019e+00 1.014e-01 1.712e+04 10.054 < 2e-16 ***
## Category2-1:Hemisphere2-1 4.041e-01 3.309e-01 3.480e+01 1.221 0.230
## Category2-1:DuraBlock2-1 -1.453e-01 2.031e-01 1.828e+04 -0.716 0.474
## Category2-1:DuraBlock3-2 1.908e+00 2.018e-01 1.459e+04 9.454 < 2e-16 ***
## Hemisphere2-1:DuraBlock2-1 -1.971e-01 2.024e-01 1.765e+04 -0.974 0.330
## Hemisphere2-1:DuraBlock3-2 5.056e-02 1.997e-01 1.025e+04 0.253 0.800
## Category2-1:Hemisphere2-1:DuraBlock2-1 3.992e-01 4.045e-01 1.739e+04 0.987 0.324
## Category2-1:Hemisphere2-1:DuraBlock3-2 1.533e-02 3.962e-01 6.960e+03 0.039 0.969
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ctg2-1 Hms2-1 DrB2-1 DrB3-2 Ct2-1:H2-1 C2-1:DB2 C2-1:DB3 H2-1:DB2 H2-1:DB3 C2-1:H2-1:DB2
## Category2-1 -0.418
## Hemisphr2-1 -0.044 -0.061
## DuraBlck2-1 0.029 0.013 0.001
## DuraBlck3-2 -0.070 -0.126 0.004 -0.651
## Ctg2-1:H2-1 -0.228 -0.101 -0.415 0.002 0.009
## Ct2-1:DB2-1 0.004 0.089 0.001 0.444 -0.245 0.002
## Ct2-1:DB3-2 -0.041 -0.207 0.005 -0.246 0.345 0.011 -0.656
## Hm2-1:DB2-1 0.000 0.001 0.075 -0.002 -0.001 0.024 -0.002 -0.001
## Hm2-1:DB3-2 0.002 0.006 -0.162 -0.001 -0.009 -0.154 -0.002 -0.010 -0.667
## C2-1:H2-1:DB2 0.000 0.002 0.014 -0.002 -0.002 0.131 -0.003 -0.002 0.440 -0.254
## C2-1:H2-1:DB3 0.002 0.008 -0.090 -0.001 -0.010 -0.267 -0.002 -0.013 -0.255 0.325 -0.674anova(lmm.opt.N170.high.E205)4.4.2.6 Diagnostic plots
qqplot_lmer(lmm.opt.N170.high.E205)
4.4.2.7 Estimated marginal means
emm.N170.high.E205 <- emmeans(lmm.opt.N170.high.E205, ~ DuraBlock | Category + Hemisphere)
emm.N170.high.E205## Category = face, Hemisphere = Left:
## DuraBlock emmean SE df lower.CL upper.CL
## 17_1 -2.449953 0.4543250 28.82 -3.379405 -1.5205000
## 17_2 -3.051512 0.4658692 31.86 -4.000617 -2.1024077
## 200_2 -3.007625 0.4549179 28.98 -3.938059 -2.0771908
##
## Category = house, Hemisphere = Left:
## DuraBlock emmean SE df lower.CL upper.CL
## 17_1 -1.429557 0.4056330 38.52 -2.250358 -0.6087573
## 17_2 -2.376014 0.4231693 45.76 -3.227932 -1.5240962
## 200_2 -0.431652 0.3761813 28.76 -1.201308 0.3380050
##
## Category = face, Hemisphere = Right:
## DuraBlock emmean SE df lower.CL upper.CL
## 17_1 -2.790329 0.4736852 28.54 -3.759797 -1.8208619
## 17_2 -3.788595 0.4847723 31.31 -4.776894 -2.8002960
## 200_2 -3.701812 0.4743015 28.70 -4.672301 -2.7313228
##
## Category = house, Hemisphere = Right:
## DuraBlock emmean SE df lower.CL upper.CL
## 17_1 -1.637055 0.3452892 42.41 -2.333678 -0.9404319
## 17_2 -2.581063 0.3668471 54.40 -3.316423 -1.8457034
## 200_2 -0.578478 0.3141882 29.66 -1.220446 0.0634906
##
## Degrees-of-freedom method: satterthwaite
## Confidence level used: 0.954.4.2.8 Plots
# emmip(emm.N170.high.E205, Category ~ DuraBlock | Hemisphere, CIs = TRUE) +
# scale_y_continuous(trans = "reverse")n170.high.LinePlot = {
ggplot(data = data.frame(emm.N170.high.E205), aes(y = emmean, x = DuraBlock, color = Category, group = Category)) + # set the data, varialbes for x and y axises, and the variable for dividing data
geom_point(size = 2) +
geom_line(aes(linetype = Category), size = .85) + # position = "dodge", alpha = .7
scale_linetype_manual(values=c("solid", "dashed")) +
geom_errorbar(mapping = aes(ymin = lower.CL, ymax = upper.CL), linetype = 1, # set the error bar
show.legend = FALSE, width = 0.25, alpha = .5) + # , position = position_dodge(width=0.9)
facet_grid(. ~ Hemisphere, # switch = "x",
labeller = labeller(Hemisphere = hemiLabel)) +
scale_y_reverse(limits = ylimit.n170, breaks = seq(1, -5, -1)) + # set the limit for y axis
scale_x_discrete(labels = durablock.labels) +
# scale_y_continuous(expand= c(0, 0)) + # remove the space between columns and x axis
labs(x = "Duration and Subjective Confidence", y = expression(paste("Amplitudes (", mu, "V)")), fill = "Category") + # set the names for main, x and y axises title = "",
# scale_x_discrete(labels = c("left", "right")) +
# geom_text(label = c("", "", "", "", "***", "", "", ""), color = "red", size = 6, nudge_y = 0.5, nudge_x = 0.5) + # add starts to the significant columns
theme_bw() +
erp_theme
}
# n170.high.LinePlot.slide <- {
# n170.high.LinePlot +
# scale_y_reverse(limits = c(0.8, -4.8), breaks = seq(0, -4, -1)) + # set the limit for y axis
# theme(legend.position="right",
# axis.line = element_line(color="black", size = 3))
# }
# ggsave('N170_high.png', n170.high.LinePlot.slide, width = 7.5, height = 4)
n170.high.LinePlot
4.4.2.9 Contrast
# contra.high.N170 <- contrast(emmeans(lmm.opt.N170.high.E205, ~ Category + DuraBlock), "pairwise", simple = "each")
#
# contra.high.N170
#
# confint(contra.high.N170)contra.high.N170.gen <- contrast(emmeans(lmm.opt.N170.high.E205, ~ DuraBlock | Hemisphere + Category), #
"pairwise", adjust = "Bonferroni")
contra.high.N170.gen[1:3]## contrast Hemisphere Category estimate SE df t.ratio p.value
## 17_1 - 17_2 Left face 0.6015598 0.1514381 18306.67 3.972 0.0002
## 17_1 - 200_2 Left face 0.5576724 0.1272494 17686.67 4.383 <.0001
## 17_2 - 200_2 Left face -0.0438874 0.1636515 18181.18 -0.268 1.0000
##
## Degrees-of-freedom method: satterthwaite
## P value adjustment: bonferroni method for 3 testsconfint(contra.high.N170.gen[1:3])contra.high.N170.gen[4:6]## contrast Hemisphere Category estimate SE df t.ratio p.value
## 17_1 - 17_2 Right face 0.9982659 0.1514036 18303.96 6.593 <.0001
## 17_1 - 200_2 Right face 0.9114827 0.1270620 17334.05 7.174 <.0001
## 17_2 - 200_2 Right face -0.0867832 0.1635189 18079.17 -0.531 1.0000
##
## Degrees-of-freedom method: satterthwaite
## P value adjustment: bonferroni method for 3 testsconfint(contra.high.N170.gen[4:6])contra.high.N170.gen.hemi <- contrast(emmeans(lmm.opt.N170.high.E205, ~ DuraBlock + Hemisphere | Category), #
interaction = "pairwise", adjust = "Bonferroni")
contra.high.N170.gen.hemi## Category = face:
## DuraBlock_pairwise Hemisphere_pairwise estimate SE df t.ratio p.value
## 17_1 - 17_2 Left - Right -0.3967060 0.2140373 18283.98 -1.853 0.1915
## 17_1 - 200_2 Left - Right -0.3538102 0.1794494 16921.73 -1.972 0.1460
## 17_2 - 200_2 Left - Right 0.0428958 0.2310594 17949.51 0.186 1.0000
##
## Category = house:
## DuraBlock_pairwise Hemisphere_pairwise estimate SE df t.ratio p.value
## 17_1 - 17_2 Left - Right 0.0024481 0.3434039 16785.99 0.007 1.0000
## 17_1 - 200_2 Left - Right 0.0606714 0.2719402 1429.15 0.223 1.0000
## 17_2 - 200_2 Left - Right 0.0582233 0.3237972 5134.82 0.180 1.0000
##
## Degrees-of-freedom method: satterthwaite
## P value adjustment: bonferroni method for 3 testscontra.high.N170.spec <- contrast(emmeans(lmm.opt.N170.high.E205, ~ Category + DuraBlock | Hemisphere), #
interaction = "pairwise", adjust = "Bonferroni")
contra.high.N170.spec[1:3]## Category_pairwise DuraBlock_pairwise Hemisphere estimate SE df t.ratio p.value
## face - house 17_1 - 17_2 Left -0.3448969 0.2869752 18209.03 -1.202 0.6883
## face - house 17_1 - 200_2 Left 1.5555782 0.2363195 7112.00 6.583 <.0001
## face - house 17_2 - 200_2 Left 1.9004751 0.2846656 13731.61 6.676 <.0001
##
## Degrees-of-freedom method: satterthwaite
## P value adjustment: bonferroni method for 3 testsconfint(contra.high.N170.spec[1:3])contra.high.N170.spec[4:6]## Category_pairwise DuraBlock_pairwise Hemisphere estimate SE df t.ratio p.value
## face - house 17_1 - 17_2 Right 0.0542572 0.2862422 17571.01 0.190 1.0000
## face - house 17_1 - 200_2 Right 1.9700598 0.2297485 2326.23 8.575 <.0001
## face - house 17_2 - 200_2 Right 1.9158026 0.2809292 7615.60 6.820 <.0001
##
## Degrees-of-freedom method: satterthwaite
## P value adjustment: bonferroni method for 3 testsconfint(contra.high.N170.spec[4:6])contra.high.N170.spec.hemi <- contrast(emmeans(lmm.opt.N170.high.E205, ~ Category + DuraBlock + Hemisphere), #
interaction = "pairwise", adjust = "Bonferroni")
contra.high.N170.spec.hemi## Category_pairwise DuraBlock_pairwise Hemisphere_pairwise estimate SE df t.ratio p.value
## face - house 17_1 - 17_2 Left - Right -0.3991541 0.4044924 17388.02 -0.987 0.9713
## face - house 17_1 - 200_2 Left - Right -0.4144816 0.3231755 2014.65 -1.283 0.5994
## face - house 17_2 - 200_2 Left - Right -0.0153275 0.3961737 6959.66 -0.039 1.0000
##
## Degrees-of-freedom method: satterthwaite
## P value adjustment: bonferroni method for 3 tests5 Appendix
5.1 Model selection procedure
For the analysis for every dependent variable, an optimal model was obtained by the following general steps:
- Maximum Model: a maximum model with all fixed and random factors was built.
- ZCP Model: a zero-correlated-parameter (zcp) model based on the maximum model is built. The only difference between zcp and maximum models is that the correlations between random effects are forced to be zero in the zcp model.
- Reduced Model: the random effects which are not supported by the data will be removed from the zcp model. The function
step(fit, fixed.reduce = FALSE)fromlibrary(lmerTest)is used for this step. The algorithm could be found here. - Extended Model: extending the reduced model with correlation parameters between the remaining random effects.
- Pruning Model: pruning low correlation parameters. (Usually I don’t do this.)
More details about this whole process chould be found here: Bates, D., Kliegl, R., Vasishth, S., & Baayen, H. (2015). Parsimonious Mixed Models. Retrieved from http://arxiv.org/abs/1506.04967.
5.2 Algorithm of function step
The outline of the algorithm of step(fit, reduce.fixed = false) function is:
Simplification of the random effects structure:
Let M be the linear mixed effects model specified by a user.
If there are random effects in M then go to 3, otherwise stop.
- For each random effect ri in M do:
Create a reduced model Mi by eliminating ri from M.
Calculate pi, the p value from the likelihood ratio test of comparing M to Mi.
Save pi and Mi.
Find pmax; the maximum of all pi and let Mmax denote the corresponding model.
Set M to Mmax. If pmax is guesser than α level then go back to 3, otherwise stop.
More deatils could be found: Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. (2017). lmerTest Package: Tests in Linear Mixed Effects Models. Journal of Statistical Software, 82(13), 1-26. http://doi.org/10.18637/jss.v082.i13 Page 8
6 Versions of packages used
# rstudioapi::versionInfo()
sessionInfo()## R version 3.6.1 (2019-07-05)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Mojave 10.14.5
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggpubr_0.2.5 emmeans_1.4.2 lmerTest_3.1-0 lme4_1.1-21 Matrix_1.2-17 magrittr_1.5 forcats_0.4.0
## [8] stringr_1.4.0 dplyr_0.8.3 purrr_0.3.3 readr_1.3.1 tidyr_1.0.0 tibble_2.1.3 ggplot2_3.2.1
## [15] tidyverse_1.2.1
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.3 lubridate_1.7.4 mvtnorm_1.0-11 lattice_0.20-38 assertthat_0.2.1 zeallot_0.1.0
## [7] digest_0.6.22 R6_2.4.0 cellranger_1.1.0 plyr_1.8.4 backports_1.1.5 evaluate_0.14
## [13] coda_0.19-3 highr_0.8 httr_1.4.1 pillar_1.4.2 rlang_0.4.1 lazyeval_0.2.2
## [19] readxl_1.3.1 rstudioapi_0.10 minqa_1.2.4 nloptr_1.2.1 rmarkdown_1.16 labeling_0.3
## [25] splines_3.6.1 munsell_0.5.0 broom_0.5.2 compiler_3.6.1 numDeriv_2016.8-1.1 modelr_0.1.5
## [31] xfun_0.10 pkgconfig_2.0.3 htmltools_0.4.0 tidyselect_0.2.5 crayon_1.3.4 withr_2.1.2
## [37] MASS_7.3-51.4 grid_3.6.1 nlme_3.1-140 jsonlite_1.6 xtable_1.8-4 gtable_0.3.0
## [43] lifecycle_0.1.0 scales_1.0.0 estimability_1.3 cli_1.1.0 stringi_1.4.3 ggsignif_0.6.0
## [49] reshape2_1.4.3 xml2_1.2.2 generics_0.0.2 vctrs_0.2.0 cowplot_1.0.0 boot_1.3-22
## [55] tools_3.6.1 glue_1.3.1 hms_0.5.2 yaml_2.2.0 colorspace_1.4-1 rvest_0.3.5
## [61] knitr_1.25 haven_2.2.0A work by Haiyang Jin